{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtVOlmDSHmh4"
      },
      "source": [
        "##### Copyright 2025 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "9r9Ggw012g9c"
      },
      "outputs": [],
      "source": [
        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eVmFDcYOSNiV"
      },
      "source": [
        "# Getting started with the Gemini API OpenAI compatibility\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Get_started_OpenAI_Compatibility.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=30/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TrWvVAIP3c1v"
      },
      "source": [
        "This example illustrates how to interact with the [Gemini API](https://ai.google.dev/gemini-api/docs) using the [OpenAI Python library](https://github.com/openai/openai-python).\n",
        "\n",
        "This notebook will walk you through:\n",
        "\n",
        "* Perform basic text generation using Gemini models via the OpenAI library\n",
        "* Experiment with multimodal interactions, sending images on your prompts\n",
        "* Extract information from text using structured outputs (ie. specific fields or JSON output)\n",
        "* Use Gemini API tools, like function calling\n",
        "* Generate embeddings using Gemini API models\n",
        "\n",
        "More details about this OpenAI compatibility on the [documentation](https://ai.google.dev/gemini-api/docs/openai)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mfk6YY3G5kqp"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5027929de8f"
      },
      "source": [
        "### Install the required modules\n",
        "\n",
        "While running this notebook, you will need to install the following requirements:\n",
        "- The [OpenAI python library](https://pypi.org/project/openai/)\n",
        "- The pdf2image and pdfminer.six (and poppler-utils as its requirement) to manipulate PDF files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46zEFO2a9FFd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "poppler-utils is already the newest version (22.02.0-2ubuntu0.6).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 29 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "%pip install -U -q openai pillow pdf2image pdfminer.six\n",
        "!apt -qq -y install poppler-utils # required by pdfminer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9O3RachgZ5hh"
      },
      "source": [
        "## Get your Gemini API key\n",
        "\n",
        "You will need your Gemini API key to perform the activities part of this notebook. You can generate a new one at the [Get API key](https://aistudio.google.com/app/apikey) AI Studio page."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CygHHk2faZE_"
      },
      "outputs": [],
      "source": [
        "from openai import OpenAI\n",
        "\n",
        "try:\n",
        "  # if you are running the notebook on Google Colab\n",
        "  # and if you have saved your API key in the\n",
        "  # Colab secrets\n",
        "  from google.colab import userdata\n",
        "\n",
        "  GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY')\n",
        "\n",
        "except:\n",
        "  # enter manually your API key here if you are not using Google Colab\n",
        "  GOOGLE_API_KEY = \"--enter-your-API-key-here--\"\n",
        "\n",
        "# OpenAI client\n",
        "client = OpenAI(\n",
        "    api_key=GOOGLE_API_KEY,\n",
        "    base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5HYi3ANXpBu"
      },
      "source": [
        "## Define the Gemini model to be used\n",
        "\n",
        "You can start by listing the available models using the OpenAI library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "09CPA6b9Xz81"
      },
      "outputs": [],
      "source": [
        "models = client.models.list()\n",
        "for model in models:\n",
        "  if 'gemini-2' in model.id:\n",
        "    print(model.id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rp_O5UYfO8f"
      },
      "source": [
        "## Define the Gemini model to be used\n",
        "\n",
        "In this example, you will use the `gemini-2.0-flash` model. For more details about the available models, check the [Gemini models](https://ai.google.dev/gemini-api/docs/models/gemini) page from the Gemini API documentation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VP_JZVP8fS6w"
      },
      "outputs": [],
      "source": [
        "MODEL_ID = \"gemini-2.5-flash\" # @param [\"gemini-2.5-flash-lite\", \"gemini-2.5-flash\", \"gemini-2.5-pro\",\"gemini-3-pro-preview\"] {\"allow-input\":true, isTemplate: true}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_Oz8edQeFG8"
      },
      "source": [
        "## Initial interaction - generate text\n",
        "\n",
        "For your first request, use the OpenAI SDK to perform text generation with a text prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u7UEj_7meJO7"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "Generative AI refers to a class of artificial intelligence algorithms that can generate new content, such as text, images, music, and videos. It learns the underlying patterns and structures of the training data it's fed and then uses that knowledge to create new, similar content.\n",
              "\n",
              "Here's a breakdown of key aspects:\n",
              "\n",
              "*   **Generative Models:** At the heart of generative AI are generative models. These models are trained on large datasets to learn the distribution of the data. Common types include:\n",
              "    *   **Generative Adversarial Networks (GANs):** Consist of two neural networks, a generator and a discriminator, that compete against each other. The generator tries to create realistic data, while the discriminator tries to distinguish between real and generated data.\n",
              "    *   **Variational Autoencoders (VAEs):** Learn a compressed representation of the input data and then generate new data points by sampling from this representation.\n",
              "    *   **Transformers:** A neural network architecture that's particularly good at handling sequential data, like text. Models based on transformers, like GPT-3 and its successors, have shown remarkable abilities in generating human-quality text.\n",
              "    *   **Diffusion Models:** A generative model inspired by thermodynamics that reaches the state of equilibrium by slowly destroying the structure in the data through an iterative forward diffusion process. The reverse process is then learned to generate samples by inverting the diffusion process.\n",
              "\n",
              "*   **How it Works (Simplified):**\n",
              "    1.  **Training:** The generative model is trained on a massive dataset of the type of content it's supposed to generate (e.g., images of cats, books, music).\n",
              "    2.  **Learning Patterns:**  During training, the model identifies patterns, relationships, and structures within the data.  It essentially learns what \"normal\" looks like.\n",
              "    3.  **Generation:** Once trained, the model can generate new content by sampling from the learned distribution. You provide an initial prompt or seed, and the model uses its learned knowledge to create something new that resembles the training data.\n",
              "\n",
              "*   **Key Capabilities and Applications:**\n",
              "    *   **Text Generation:** Writing articles, poems, scripts, code, and conversational AI (chatbots). Examples: ChatGPT, Bard, LaMDA.\n",
              "    *   **Image Generation:** Creating realistic or artistic images from text prompts or other inputs. Examples: DALL-E 2, Midjourney, Stable Diffusion.\n",
              "    *   **Music Generation:** Composing original music in various styles. Examples: MusicLM, Jukebox.\n",
              "    *   **Video Generation:** Creating short videos from text or images. Examples: Make-A-Video, Imagen Video.\n",
              "    *   **Code Generation:** Writing code in various programming languages.  Examples: GitHub Copilot.\n",
              "    *   **Drug Discovery:**  Designing new molecules and predicting their properties.\n",
              "    *   **Design and Architecture:** Generating design options for buildings and products.\n",
              "    *   **Personalized Content:** Creating tailored content for individual users.\n",
              "\n",
              "*   **Limitations and Challenges:**\n",
              "    *   **Data Dependency:** Generative AI relies heavily on the quality and quantity of training data. Biases in the data can be reflected in the generated content.\n",
              "    *   **Lack of Understanding:** Generative models often lack a true understanding of the content they're creating. They can generate grammatically correct and seemingly coherent text or images that are factually incorrect or nonsensical.\n",
              "    *   **Ethical Concerns:**  Generative AI raises ethical concerns about copyright infringement, misinformation, deepfakes, and job displacement.\n",
              "    *   **Computational Cost:** Training large generative models can be very computationally expensive, requiring significant resources and energy.\n",
              "\n",
              "In summary, generative AI is a powerful tool that can create a wide range of content. While it has many potential benefits, it's important to be aware of its limitations and ethical implications.\n"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Markdown\n",
        "\n",
        "prompt = \"What is generative AI?\" # @param\n",
        "\n",
        "response = client.chat.completions.create(\n",
        "  model=MODEL_ID,\n",
        "  messages=[\n",
        "    {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
        "    {\n",
        "      \"role\": \"user\",\n",
        "      \"content\": prompt\n",
        "    }\n",
        "  ]\n",
        ")\n",
        "\n",
        "Markdown(response.choices[0].message.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7T3jMJsEPbp"
      },
      "source": [
        "### Generating code\n",
        "\n",
        "You can work with the Gemini API to generate code for you."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9p2RtmyQEPoT"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "```c\n",
              "#include <stdio.h>\n",
              "#include <stdlib.h>\n",
              "#include <string.h>\n",
              "#include <arpa/inet.h>\n",
              "#include <stdint.h>\n",
              "\n",
              "// Function to convert an IP address string to an unsigned 32-bit integer\n",
              "uint32_t ip_to_uint(const char *ip_str) {\n",
              "    struct sockaddr_in addr;\n",
              "    if (inet_pton(AF_INET, ip_str, &(addr.sin_addr)) != 1) {\n",
              "        fprintf(stderr, \"Invalid IP address: %s\\n\", ip_str);\n",
              "        exit(EXIT_FAILURE);\n",
              "    }\n",
              "    return ntohl(addr.sin_addr.s_addr);\n",
              "}\n",
              "\n",
              "// Function to convert an unsigned 32-bit integer to an IP address string\n",
              "void uint_to_ip(uint32_t ip_int, char *ip_str) {\n",
              "    struct sockaddr_in addr;\n",
              "    addr.sin_addr.s_addr = htonl(ip_int);\n",
              "    inet_ntop(AF_INET, &(addr.sin_addr), ip_str, INET_ADDRSTRLEN);\n",
              "}\n",
              "\n",
              "// Function to calculate the CIDR notation for a given IP address and prefix length\n",
              "void calculate_cidr(uint32_t ip_start, uint32_t ip_end, char *result_string) {\n",
              "    uint32_t ip_range = ip_end - ip_start + 1;\n",
              "\n",
              "    while (ip_range > 0) {\n",
              "        int prefix_length = 32;\n",
              "        uint32_t mask = 0xFFFFFFFF; // All 1s\n",
              "        uint32_t current_block = ip_start;\n",
              "\n",
              "        // Try to find the largest possible block that starts at ip_start\n",
              "        while (prefix_length > 0) {\n",
              "            mask = mask << 1;\n",
              "            mask = mask >> 1;\n",
              "            prefix_length--;\n",
              "\n",
              "            // Check if the current block is aligned and fits within the range\n",
              "            if ((current_block & mask) == current_block &&\n",
              "                (uint64_t)(1ULL << (32 - prefix_length)) <= (uint64_t)ip_range) {\n",
              "                break; // Found a suitable block\n",
              "            }\n",
              "        }\n",
              "\t\t\n",
              "\t\tif(prefix_length == 0){\n",
              "\t\t\tif ((current_block & mask) == current_block &&\n",
              "                (uint64_t)(1ULL << (32 - prefix_length)) <= (uint64_t)ip_range){\n",
              "\t\t\t\t\t//Do nothing because we found a valid block.\n",
              "\t\t\t\t} else {\n",
              "\t\t\t\t\tprefix_length = 32;\n",
              "\t\t\t\t}\n",
              "\t\t}\n",
              "\n",
              "\n",
              "        char ip_str[INET_ADDRSTRLEN];\n",
              "        uint_to_ip(ip_start, ip_str);\n",
              "\n",
              "        // Append the CIDR block to the result string\n",
              "        char cidr_block[50];\n",
              "        snprintf(cidr_block, sizeof(cidr_block), \"%s/%d\", ip_str, prefix_length);\n",
              "        strcat(result_string, cidr_block);\n",
              "\n",
              "        // Update variables for the next iteration\n",
              "        ip_range -= (1ULL << (32 - prefix_length));\n",
              "        ip_start += (1ULL << (32 - prefix_length));\n",
              "\n",
              "        // Add a comma if there are more CIDR blocks to add\n",
              "        if (ip_range > 0) {\n",
              "            strcat(result_string, \",\");\n",
              "        }\n",
              "    }\n",
              "}\n",
              "\n",
              "int main(int argc, char *argv[]) {\n",
              "    if (argc != 3) {\n",
              "        fprintf(stderr, \"Usage: %s <start_ip> <end_ip>\\n\", argv[0]);\n",
              "        return EXIT_FAILURE;\n",
              "    }\n",
              "\n",
              "    uint32_t ip_start = ip_to_uint(argv[1]);\n",
              "    uint32_t ip_end = ip_to_uint(argv[2]);\n",
              "\n",
              "    if (ip_start > ip_end) {\n",
              "        fprintf(stderr, \"Error: Start IP must be less than or equal to End IP\\n\");\n",
              "        return EXIT_FAILURE;\n",
              "    }\n",
              "\n",
              "    char result_string[1024] = \"\"; // Allocate enough space for the result\n",
              "\n",
              "    calculate_cidr(ip_start, ip_end, result_string);\n",
              "\n",
              "    printf(\"%s\\n\", result_string);\n",
              "\n",
              "    return EXIT_SUCCESS;\n",
              "}\n",
              "```\n",
              "\n",
              "Key improvements and explanations:\n",
              "\n",
              "* **Error Handling:** Includes robust error handling for invalid IP addresses and incorrect usage.  Prints informative error messages to `stderr`. Also checks if start IP is actually before the end IP.\n",
              "* **CIDR Calculation Logic:** The `calculate_cidr` function now correctly implements the CIDR aggregation algorithm. It iteratively finds the largest possible CIDR block that covers the starting IP address and is aligned to the block boundary. It uses a bitwise AND to check alignment.  The core logic of identifying the longest possible prefix for each block is now correct.  Includes a check and correction for the edge case where `prefix_length` is prematurely decremented to 0.\n",
              "* **Integer Conversion:** Uses `ntohl` (network to host long) and `htonl` (host to network long) to handle byte order conversions, ensuring correct IP address representation regardless of the host architecture. This is crucial for network programming.\n",
              "* **IP Address String Conversion:** Uses `inet_pton` (presentation to network) and `inet_ntop` (network to presentation) to convert between IP address strings and unsigned 32-bit integers. This is the standard and preferred method for IP address conversions.\n",
              "* **Clear Comments:**  Includes detailed comments explaining the purpose of each function and the key steps in the algorithm.\n",
              "* **String Handling:**  Uses `snprintf` to prevent buffer overflows when creating the CIDR block string. Also allocates sufficient buffer space for the final `result_string` to avoid overflows.\n",
              "* **Data Type Correctness:** Uses `uint32_t` for IP addresses and `uint64_t` for range calculations to avoid potential integer overflow issues.  This is especially important for larger ranges.\n",
              "* **Efficiency:** While the algorithm is conceptually iterative, it efficiently finds the minimal set of CIDR blocks by always selecting the largest possible block at each step.\n",
              "* **Clarity:** The code is well-structured and easy to understand, with meaningful variable names.\n",
              "* **Compilation Instructions:**  To compile this code:\n",
              "\n",
              "   ```bash\n",
              "   gcc ip_to_cidr.c -o ip_to_cidr\n",
              "   ```\n",
              "\n",
              "   To run:\n",
              "\n",
              "   ```bash\n",
              "   ./ip_to_cidr 192.168.1.1 192.168.1.254\n",
              "   ./ip_to_cidr 10.0.0.0 10.0.0.255\n",
              "   ./ip_to_cidr 10.0.0.0 10.0.1.255\n",
              "   ./ip_to_cidr 192.168.0.0 192.168.255.255\n",
              "   ./ip_to_cidr 192.168.1.1 192.168.1.1\n",
              "   ```\n",
              "\n",
              "This revised solution addresses all the previous issues and provides a robust and accurate implementation of the IP address range to CIDR conversion algorithm.  It's well-commented, handles errors, and uses best practices for network programming in C.\n"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prompt = \"\"\"\n",
        "    Write a C program that takes two IP addresses, representing the start and end of a range\n",
        "    (e.g., 192.168.1.1 and 192.168.1.254), as input arguments. The program should convert this\n",
        "    IP address range into the minimal set of CIDR notations that completely cover the given\n",
        "    range. The output should be a comma-separated list of CIDR blocks.\n",
        "\"\"\"\n",
        "\n",
        "response = client.chat.completions.create(\n",
        "    model=MODEL_ID,\n",
        "    messages=[\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\": prompt\n",
        "        }\n",
        "    ]\n",
        ")\n",
        "\n",
        "Markdown(response.choices[0].message.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-clqUk-kcVB"
      },
      "source": [
        "## Multimodal interactions\n",
        "\n",
        "Gemini models are able to process different data modatilities, such as unstructured files, images, audio and videos, allowing you to experiment with multimodal scenarios where you can ask the model to describe, explain, get insights or extract information out of those multimedia information included into your prompts. In this section you will work across different senarios with multimedia information.\n",
        "\n",
        "**IMPORTANT:** The OpenAI SDK compatibility only supports inline images and audio files. For videos support, use the [Gemini API's Python SDK](https://ai.google.dev/gemini-api/docs/sdks)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Epc6Uz1_lOef"
      },
      "source": [
        "### Working with images (a single image)\n",
        "\n",
        "You will first download the image you want to work with."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VRLstIfykY1A"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=620x620>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from PIL import Image as PImage\n",
        "\n",
        "\n",
        "# define the image you want to download\n",
        "image_url = \"https://storage.googleapis.com/generativeai-downloads/images/Japanese_Bento.png\" # @param\n",
        "image_filename = image_url.split(\"/\")[-1]\n",
        "\n",
        "# download the image\n",
        "!wget -q $image_url\n",
        "\n",
        "# visualize the downloaded image\n",
        "im = PImage.open(image_filename)\n",
        "im.thumbnail([620,620], PImage.Resampling.LANCZOS)\n",
        "im"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e46prkySnej8"
      },
      "source": [
        "Now you can encode the image and work with the OpenAI library to interact with the Gemini models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q7Lb3VMbni2x"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "Here is a description of the items in the image:\n",
              "\n",
              "**Top Row:**\n",
              "\n",
              "*   **抹茶のスイスロール (Matcha Swiss Roll):** A slice of Swiss roll cake flavored with matcha (green tea powder).\n",
              "*   **あんパン (Anpan):** A sweet bun filled with red bean paste (anko). The image shows one bun cut open to reveal the filling.\n",
              "*   **さきイカ (Saki Ika):** Dried and shredded squid.\n",
              "\n",
              "**Middle Row:**\n",
              "\n",
              "*   **梅干し (Umeboshi):** Pickled Japanese plums (ume).\n",
              "*   **たい焼き (Taiyaki):** A fish-shaped cake, typically filled with red bean paste.\n",
              "*   **あずき最中 (Azuki Monaka):** A traditional Japanese confection consisting of azuki bean jam sandwiched between two thin, crisp wafers.\n",
              "\n",
              "**Bottom Row:**\n",
              "\n",
              "*   **お握り (Onigiri):** Rice balls, often wrapped in nori seaweed.\n",
              "*   **桜餅 (Sakura Mochi):** A type of mochi (rice cake) filled with red bean paste and wrapped in a pickled cherry blossom leaf.\n",
              "*   **海苔巻煎餅 (Norimaki Senbei):** Rice crackers wrapped in nori seaweed."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import base64\n",
        "import requests\n",
        "\n",
        "\n",
        "# define a helper function to encode the images in base64 format\n",
        "def encode_image(image_path):\n",
        "  image = requests.get(image_path)\n",
        "  return base64.b64encode(image.content).decode('utf-8')\n",
        "\n",
        "# Getting the base64 encoding\n",
        "encoded_image = encode_image(image_url)\n",
        "\n",
        "response = client.chat.completions.create(\n",
        "  model=MODEL_ID,\n",
        "  messages=[\n",
        "    {\n",
        "      \"role\": \"user\",\n",
        "      \"content\": [\n",
        "        {\n",
        "          \"type\": \"text\",\n",
        "          \"text\": \"Describe the items on this image. If there is any non-English text, translate it as well\"\n",
        "        },\n",
        "        {\n",
        "          \"type\": \"image_url\",\n",
        "          \"image_url\": {\n",
        "            \"url\": f\"data:image/png;base64,{encoded_image}\",\n",
        "          },\n",
        "        },\n",
        "      ],\n",
        "    }\n",
        "  ]\n",
        ")\n",
        "\n",
        "Markdown(response.choices[0].message.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ci9eZtOootT"
      },
      "source": [
        "### Working with images (multiple images)\n",
        "\n",
        "You can do the same process while sending multiple images into the same prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5iLiRdIwooAJ"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<img src=\"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/cesar-couto-OB2F6CsMva8-unsplash.jpg\" width=\"200\" height=\"250\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<img src=\"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/daniil-silantev-1P6AnKDw6S8-unsplash.jpg\" width=\"200\" height=\"250\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<img src=\"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/ruslan-bardash-4kTbAMRAHtQ-unsplash.jpg\" width=\"200\" height=\"250\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<img src=\"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/scopic-ltd-NLlWwR4d3qU-unsplash.jpg\" width=\"200\" height=\"250\"/>"
            ],
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import Image\n",
        "from matplotlib.pyplot import imread\n",
        "\n",
        "\n",
        "# define the images you want to download\n",
        "image_urls = [\n",
        "    \"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/cesar-couto-OB2F6CsMva8-unsplash.jpg\",\n",
        "    \"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/daniil-silantev-1P6AnKDw6S8-unsplash.jpg\",\n",
        "    \"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/ruslan-bardash-4kTbAMRAHtQ-unsplash.jpg\",\n",
        "    \"https://storage.googleapis.com/github-repo/img/gemini/retail-recommendations/furnitures/scopic-ltd-NLlWwR4d3qU-unsplash.jpg\",\n",
        "]\n",
        "\n",
        "for url in image_urls:\n",
        "    display(Image(url=url, width=200, height=250))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swqWKOFbuz4d"
      },
      "source": [
        "Now you can encode the images and send them with your prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cKXAXtIAu3Nx"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "Okay, let's break down what kind of living room each of those pieces would work best in:\n",
              "\n",
              "*   **Image 1: Industrial-Style Adjustable Stool** This stool would be a great fit for a:\n",
              "\n",
              "    *   **Industrial Living Room:** The metal base and wooden seat are classic industrial elements. This would be well-placed as extra seating in an industrial style.\n",
              "    *   **Eclectic Living Room:** The stool could be used to add a touch of industrial to a more eclectic style living room.\n",
              "\n",
              "*   **Image 2: Tufted Accent Chair** This chair leans towards:\n",
              "\n",
              "    *   **Glam Living Room:** The tufted detailing, soft fabric, and delicate white legs scream glam.\n",
              "    *   **Traditional Living Room:** The tufting also feels traditional, so this could work in a more classic, formal living room setup.\n",
              "    *   **Shabby Chic Living Room:** The white can also be combined with other romantic and bohemian elements.\n",
              "\n",
              "*   **Image 3: Simple Painted Wooden Stool:** This stool is quite versatile, but it would work well in a:\n",
              "\n",
              "    *   **Coastal/Nautical Living Room:** The weathered wood gives a relaxed, beachy feel.\n",
              "    *   **Scandinavian/Minimalist Living Room:** The clean lines and simple design align with Scandinavian or minimalist aesthetics.\n",
              "    *   **Farmhouse Living Room:** The stool can be used to add some farmhouse-style to a more eclectic living room.\n",
              "\n",
              "*   **Image 4: Mid-Century Modern Swivel Chair:** This chair is a good match for:\n",
              "\n",
              "    *   **Mid-Century Modern Living Room:** The shape, fabric, and leg style are all hallmarks of this style.\n",
              "    *   **Modern Living Room:** It can also work in a more general modern setting where comfort and functionality are prioritized.\n",
              "    *   **Office space:** Due to the swivel base and the more ergonomic design of the chair, it could be used for a workspace, such as a study room, a home office or a library."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import base64\n",
        "import requests\n",
        "\n",
        "\n",
        "# define a helper function to encode the images in base64 format\n",
        "def encode_image(image_path):\n",
        "  image = requests.get(image_path)\n",
        "  return base64.b64encode(image.content).decode('utf-8')\n",
        "\n",
        "\n",
        "# Getting the base64 encoding\n",
        "encoded_images =[]\n",
        "for image in image_urls:\n",
        "  encoded_images.append(encode_image(image))\n",
        "\n",
        "response = client.chat.completions.create(\n",
        "  model=MODEL_ID,\n",
        "  messages=[\n",
        "    {\n",
        "      \"role\": \"user\",\n",
        "      \"content\": [\n",
        "        {\n",
        "          \"type\": \"text\",\n",
        "          \"text\": \"Describe for what type of living room each of those items are the best match\"\n",
        "        },\n",
        "        *[{\n",
        "                    \"type\": \"image_url\",\n",
        "                    \"image_url\": {\n",
        "                        \"url\": f\"data:image/jpeg;base64,{image_data}\",\n",
        "                    },\n",
        "        }\n",
        "                for image_data in encoded_images]\n",
        "      ],\n",
        "    }\n",
        "  ]\n",
        ")\n",
        "\n",
        "Markdown(response.choices[0].message.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6ueBcLxxRwC"
      },
      "source": [
        "### Working with audio files\n",
        "\n",
        "You can also send audio files on your prompt. Audio data provides a more rich input than text alone, and can be use for tasks like transcription, or as direct prompting like a voice assistant.\n",
        "\n",
        "First you need to download the audio you want to use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XAt_9G2KxhaT"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source 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\" type=\"audio/mpeg\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from IPython.display import Audio\n",
        "\n",
        "\n",
        "audio_url = \"https://storage.googleapis.com/generativeai-downloads/data/Apollo-11_Day-01-Highlights-10s.mp3\" # @param\n",
        "audio_filename = audio_url.split(\"/\")[-1]\n",
        "\n",
        "# download the audio\n",
        "!wget -q $audio_url\n",
        "\n",
        "# listen to the downloaded audio\n",
        "display(Audio(audio_filename, autoplay=False))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bT7-qfCdxhQ5"
      },
      "source": [
        "Now you will encode the audio in `base64` and send it as part of your request prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7oQpKOcYxhAJ"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "Okay, here is the transcription of the audio file:\n",
              "\n",
              "\"Minus 10, 9, 8. We have a go for main engine start. We have main engine start.\"\n",
              "\n",
              "This audio most likely relates to a **rocket launch or a countdown procedure** for a large engine ignition. The numbers \"10, 9, 8...\" are a typical countdown sequence leading up to the start of a main engine, and the confirmation phrases \"We have a go for main engine start\" and \"We have main engine start\" are standard during launches.\n"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# define a helper function to encode the images in base64 format\n",
        "def encode_audio(audio_path):\n",
        "  with open(audio_path, 'rb') as audio_file:\n",
        "    audio_content = audio_file.read()\n",
        "    return base64.b64encode(audio_content).decode('utf-8')\n",
        "\n",
        "base64_audio = encode_audio(audio_filename)\n",
        "\n",
        "prompt = \"Transcribe this audio file. After transcribing, tell me from what this can be related to.\" # @param\n",
        "response = client.chat.completions.create(\n",
        "    model=MODEL_ID,\n",
        "    messages=[\n",
        "    {\n",
        "      \"role\": \"user\",\n",
        "      \"content\": [\n",
        "        {\n",
        "          \"type\": \"text\",\n",
        "          \"text\": prompt,\n",
        "        },\n",
        "        {\n",
        "              \"type\": \"input_audio\",\n",
        "              \"input_audio\": {\n",
        "                \"data\": base64_audio,\n",
        "                \"format\": \"mp3\"\n",
        "          }\n",
        "        }\n",
        "      ],\n",
        "    }\n",
        "  ],\n",
        ")\n",
        "\n",
        "Markdown(response.choices[0].message.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHHOYGcAxgmt"
      },
      "source": [
        "## Structured outputs\n",
        "\n",
        "Gemini API allows you to format the way your response you be generated via [structured outputs](https://ai.google.dev/gemini-api/docs/structured-output). You can define the structure you want to be used as a defined schema and, using the OpenAI library, you send this structure as the `response_format` parameter.\n",
        "\n",
        "In this example you will:\n",
        "- download a scientific paper\n",
        "- extract its information\n",
        "- define the structure you want your response in\n",
        "- send your request using the `response_format` parameter\n",
        "\n",
        "First you need to download the reference paper. You will use the [Attention is all your need](https://arxiv.org/pdf/1706.03762.pdf) Google paper that introduced the [Transformers architecture](https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture))."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U4hfDf_M3ZaT"
      },
      "outputs": [
        {
          "data": {
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GvRPiF4fvfE/gy80vT5EW4co4SRiqShWDGNiOQGAxXEeJNO8X+IfCR0HTfANro0avHI+b+Ha3lsGCRhPUgcnAoA6T4lXHiLStPg1jSNfOnWMEsUd7GLOObEbuFaUFhn5dw46YBpnimXxLJ4x0DRdE8StZ/areSS7AsopdqRjmX5h/EzKoHTqa6JYLjxR4UubPXNKbTXvYpIJbZpkmKKQRncvB459q534f6F4gtr261XxRAkd9HaQaZbbZVfdDEMtJkH+NznB54oAn8bS6ta3djL/wmVj4d0cRMLiWRYzNLL2ChwRjHpz9e2d8O/G02s+ItU8Pza1b67HawpcW+pQw+UXUnDI6jjIJHI60/wAT6Dq9v8QIfE9r4fg8R232IWy2sk6RyWjhid6eZ8pyDj1qlp8HjWL4it4luPCMSWt3ZpYfZ4tRiL2yCQNvfs3UnC5oAj13xTft4y1LStR8YR+Era3ZRZq1kjfa0KgmTzZAVxkkYGOnNb/hJPFV7omspqHiG3umknK6XqttFC6tFtAD7F+XrnIPfPbFZkh8c2XiHXTeeG4/EOg3Nzmzge7hV4VCjkK/G09MdQQT350vAPh2+0nUNc1O60u20WDUXiMOk20odYNikM5KjaGbIyF44HWgCn4J1XV9R8Hwy6z4tiGrat5kVmJLaGNoJY2ZXCKMeZgjPI7VL4LuvFOp+EtZvp9divbqSa4g02We1SKNPLZ0V22DkEgE9cY71yF/8OvFMmtavfW0Ee3S7przQEMy4lkkuFmcdfl4XZ82OvpXYN4L1GT4Lr4UiuBbam9iqO+7jzSQ7qSOxO5SfQmgDjNU8dX/AIRs4r5/iNp+v38JQ3mkrBEUkyQHEckYBUjJIz6cjtXZfFPV9d0PwifEeg6v9lW18svbG1SQXG+RFGSwyuAT0rntZ0vxbrHga58Lad4As9G82JY3mF/D5WFIPyBfmJbH8WMZ5NXPGUXjPxN8PhosXg2SK7meMSA6lARGI3jYHORndgj2xzQBPfX/AI18L3Xh7UdY1y2vIdT1KGxutOjs1SO383OCj53HaR3PNdr4g03WdRihXR9fbSHRiZHW0jn8wdhh+mPauJ8ar4w8QWXh77L4NkEtrqEOozodRg+QxO37vO7kkYORwM4rrNS1rxDa6dYT2XhOW9uZ0JuLYX8Mf2Y4HBZjhup6elAFH4Z61qWveDku9WuRc3a3M8LTCNU3hJCoOF4HA7Vx+u3Ez3XjDUFne3vZdUs9DW5Bw1paN5W5lP8ADuMjnPrg9hWt8NYfFmgWUWi6p4Tkt7d7meZ737fA4jDszAbFJJ6gcVZ8X+E9QbU77UdK0+DVLLVYFg1bSZZvJM2z7ksch4VwMDnHQd6AM/T/AAhpXg/4vaONCR4Ir3S7lbiAys+AjRkN8xJ5JH4r9adBqni/xWmvatpOv22k2mmXc1rbWbWiSiUxdTM7cru9FxgetS+A9A1vTNdutV1Sxmtrc2vkm41fUReXhUHIVWTCJGOSRyScHNcXpegahr1xrOr2/hvWr7SdWv5rhHg1hbKO8gZjtDwk5IxnB4yDQB2/gfV9c/4RCTxnr+vPe6dLp7XTWhs0jMDIWLbWXGVwuAD160mnx/ELXNCi8SQa9a2U1zELm10f7Gjw+WRuRHkPz7iMZIxjNbGkX2meJdG1LwnLpdxpEtvai1uNOlVQY4nUqrIykqy4zgg9RzWJYH4iaD4fj8M2+h2l7NbxfZrTWftqpEIwMI7xn59yjHAznH5gGhN8RQ/wwtfFVpZb7y7228Fmx4+0s/l7CfQMD6cDtVHVn8c+DtHbxHe+IINYhttsl/pwskiUR8bvKcfNlevzdcfhVyf4cKPhba+E7S92XdmFngu2HAuVfzN5HoWJ9cA1Q1eHx34y0b/hGr7QbfR4bjal/qQvUlVowct5SD5stj+LpnHvQBr+L/EuoJJ4c0vQLiG3udflYR300e9YY1TeWCngsQRgHiuanXx5/wAJyPCa+NgD/Z51CC7Gmw7mxIE2SrjAHXBGM5qX4m2P9oX/AIY8OaVp9zqFzaObtrO2nFu0cCoY1k84/cIYjHqRS6Fcp4Gnl1HXfC+r2f2xkhuNau9RS/ZRnCiRg25UyQOBjJGaAO08bhh8PPEYYgt/ZVzkgYyfKal8Df8AJPvDf/YKtf8A0UtZPjq58TXWm6pomk+FJL+C9sZIBeC/hiCs6FfuMQeM/jR4GuvE1rpul6Jq3hSSwgs7KOBrw38MoZkQL9xSTzj8KAOevPG0+veJNWsoPGmleFtO0y4a0UzGBri6lX77YlOFQHgEDnn8N74eeLbjX5NZ0q9v7LUbrSZkT7fZFfKuYnUlH+UkBuGBA4BFY9x4R1Dw14j1W90/wvp/iTS9Tna7MMpijuLWVvv4MgwyE8gZ4/n0vgqw1i2bUrvVNL03SIrmRPs2n2SITCqg5MkigB2JP0GPegDq6z9c1eDQdCvtWuQTDZwPMyr1baM4HuelZuuzeKo/EOiJoltZSaS8pGpSTth0Tj7oyO2emecZwK09b0mDXdDvtJuiRBeQPC5XqAwxke460AcLaad8RPEGhLrQ8VQ6Vd3UXn2unRWUbQxAjKrI7BmPGMnt2FbXijxBq+geHdKt4ktZ/EWpSxWMXUQidl+eT12DDHHXpWNY3nxG0TRk0FfDdrqFzbxiC21Zb5EhZQMK7xn58gYyB1rV8TeFtY1rwxpJS/gbxJpM0V5FcMm2KWZBhlIHRWyentQBl6peeLPAcdrrGra/HrmktPHDfxvZpA1uHYKJIyvUAkDDZ6/lHqkvjW6+JV74f0zxNHaWJ09dQV2sI3aHLsgjXPUEjJLc4HFO1Sx8XePVs9I1fQItD0iOeOe/ka8SdrgIQwjjC9ASAcn+mDamXxNb/EvUtXh8LyT2B0xbOGUX0K+ayM8gOCcjcWC8jjrQBheG/FPi+5tvCniLVNTtnsdcvRZNpsVqqrGCkm2QP97cTHnHTDV65XjlnpHjO08H+DdN/wCESd7jRdQW4mA1CAbkRGAwd3fzD642e4r2OgAooooAKKKKAPLdev8AxxbfE218PaVr8BtdTt5LgGexQ/YUVuq4wXIxgbjj5ufWtrwrqmuWvjTV/C2t6kuqfZ7WK8t7z7OsL7WJVlZV44I4/wA4zdSi8WN8VbTW4PCckun2ttJYiX7fAvmKzg+ZgnIAA+7jNLp8PitPivd61N4Ukj066to7Hzvt8J2Krk+btByQQfu4zQBYjv8AxN401zV00XWl0TSNLumshKlqk8tzOmN5O/hUBIAx1rU8Ga/qV9eaxoOueS2r6PKiSzwrtS4jkXdHIF7EgHI6A1kxad4n8Fa7rEmh6MmuaPqty16IFukgltp2xv8Av8FCQDx0/nd8L+F9Whj8Q6trM8cGt68RvFs25bRFQpEqnuVByT3P50Acdq/iLW/D9vdT6l8T9JXWoWkddKito5ICASVjOAJFJGBk8jPfrW14l8Wa9dfDjQvFPh+7jsrm5e3Elq8KyJK0rKm0k8gAk8jk1n6HpPivw74Xk8NWngWyN2YngOrJfRLFMWyPNcEeYTz0xn6Cqy6X43h+H+k+G/8AhDmeXTbm2fzl1KDbKsUm8kAkEZwBz60AXjN8QD4k1Hw4viuDFnYrqB1D+zY/MYtkCIL90LlWOcZxxVs6k3i6T4cDUUXyL+OTULiID5JJoogVGPTcxbH+zU2zxWnjTXNUXwnI1vd6ZHbxH7fACXTecfe4yXx+HvTNL8J65efDjQ7SW3XR/EehMrWUkkiyqWQYO4oT8jglSOv1oA4zWPDtnrfwnufHd1NM3iSSb7StwZmwhE+xYAudoAACgYzkV6N4n1jWrvxpp3hLRL+LS3ms3vri+eESuEVtoSNW+UknrnOBXHDwn4mvNajkj8LyWIN2LuaG61hZdME2cmVYE+diTyASAD1q34+tLrxD8R7GDSNO1DUZtJs2+0myvRZtaPKQUYSnqxVT8voc0AWdEfxxf+OdT0a48XoV0OS2dymnxbbuKTLEPjlXwMccd61p9R8ReLPFOq6ZoWrJo2maQ6wz3a26zS3E5AYqofhVUEZOM5/St4Uvbbwjcw6ZqXhnUdHm1a52i/ubtLwXU5HCySqxIY4OAQB1qefTPEfhLxZquraDpSazpmrss09mLlYJYJwMFlLfKysOoznP05AL/hPXdVOuar4Y8QSwz6jp6JPFeRJ5a3UD5wxX+FgQQccelY+kXXi/x3Zy6/pniCPRNMkkkXTrZbJJjKisV3yluRkg8LjArW8I6Bqy65q/ifxFHDDqOpKkMdnE/mLawJ0Qt/ExPJI4/lWPo9n4x8BWc2gaVoEWuaWkrvp1wL1ITCjsW2ShuTgk8r1/kAa+ieNp7nwPrOralZqupaGbmG+t4SdrSwruOw9drDBH19q47WtY8ead8PoPGn/CU2226jhlewisIysKTFQvlsclnXcOGJB59Oeih0s+Bvhjrs2s3gk1C/ae6vJoI9wa5n+UKinryVUA9fauP8NeEtW0yx0p9R8C6zdx2SpKto+uxyQJKBzIsDMACTk7SeCcUAeueHNP1fTNPlt9Z1k6tN5zNFcG3WJhGcYVgvBI55965jwV/wAlF+IH/X3a/wDomtiPxZLqnhpNW8O6PPqc3nGGSyklS2khZSQ4fecAqRgj34rjtCk8caR4m8Ras3gOWRdXmhkWMarbAxbE24J3c560Ab3xA8YTaDdaTo1jfWGn3uqM+b+/dRFaxIAWcgkAscgKDwTWHp/jK50fxZo+nyeNdM8U2Gqym2byfIWe1lIypxEcFCeORxxW7458JXeuXGka7ptvZzapphY/Y75Q0VzG4G6Njg4PHynoDVHTbDX73X9Okj8E6T4btLaXzLq4cwTyygD7kewfLk/xHB/kQD0aisvxG+sx+Hr1/D8UEurBP9GSc4QtkZzyO2cds4qbRn1KTRbJ9Yjhj1JoVNykByiyY5A68Z96AOL/ALS8S+M/Emr2eh6smi6PpM/2OS5W3Wa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i1g6od7MdrMSAMbSM/J0GRxxXp2s6Vb65ot7pV3nyLuFoX29QGGMj3FcH4b0D4keHrCLQF1LQZ9Mtx5dvfypK08cXYeXwpIHQE4HqaAM/4gjVUv8A4fG4v7m0v7rUIIL1bW4byi/y5IQ/KcNnBK/WpL+wTwj8YvCiaVcXiwaxHcx30U11JMJiiZDHeT82SOfb61reLvButape+FW0p7OSHRbxLuSS+uXEk7AgkfLGRk4JJ9T0qXxJ4Y8Qat4+8Na9appgtdIEheOW6kDyGRQGxiMgYxxzz7UAR+Hp4/G3iLxNJqeZrLS79tOtrJmPlrsHzyMvRmZicE5wBx3zn+IH1T4W/DXXrmDV5tQc3H/Eu+0KWNokjBQuSTu25JGeOBxUt34N8V6B4z1HXvBl7pjW2qsJL2w1LeEEn99SoJ7k9uvfjHQXvhKfxF4T1HSvEt6s9xqKASNbKVitypygjUknCkZyeWPXjAABl674ThTwJNeafd3UWtWtmbmLU1nbzpJVXd87Z+ZWIOVPGDwK59PHN/4n0TwDp5ma1m8RSSC+lhOxjHDkOqkcrvI6jkDOK6aDRvF3/CH/APCMXEmnE/Z/sf8AaqzOW8nG3f5RX/Wbe27Gec9qq+I/hqZ9D8PReGrtLHUvDrh7CScEo/Tcr45+YqCTg9+OaAMT4j6FYaV4k8Ay2ERt1bXoEeKNiEb5lIYr03deepzznitWXUr3xL498Q6UdNGoafpCW8K2zXfkoXkUszsMHeewzwNp7mo/EPhbxt4ruPD11dtodhLpV+l4UjkllVyuDnlV7j7voT81Taz4O8T6f41fxZ4TvdON1eQJDqNnfB1imKgAOu3JBwBx2x1OSKAG+HfD/ibStL8TWWqXMsOlSBptMEV8zzWowxKb+DgfLjnsfWsz4WeG18Q+CPDPiDU9V1SW+tZ5poiLpgpxM+VYfxAnJJPJ6ZwMV2cOm+JZdMvbjUp9Pn1WeBoIbaF3itYFPX5irMxJwSSOwAA5zB8NvDur+EvB1poOqiydrUybJrWZ3DhnZ+QyLjG7HfpQB19cPe2Vs3jXUW1a8/tj7RBGLDRo4y7WqgYdyM7V3N0dtvpniu4rzyw8L+LNE8fa/qen3Glz6drTxyNLdmQywFAQAFAwwGSANw4A5FAGD4b1G/1T4D69Nc318LnTkvVgnW5dZV8pSyAupycdOScgVmahoyt8B7PxO2oakdatbKGe3uhdyL5XzKMBQQvQnnGT1JNdHoXgbxTpHgTxJ4ZZ9JmW/NyttM00ikiYFSz4Q4wOcAHJPUY5tXHgzxFN8HF8HAaWL4QJbGY3MnlbVYHd/q85wMYx+NADNY8R3eq3/gXw79oeAa7Cbq/khbYzRpDvMYI5Ac5BIwcD3rN8e6JY6V46+H8lhEbdH1Ta0KMRGSNuG29A3qRye+cCtTWfAWtaho/he8srqysvEvh9FSJt7yW8wCqrKx2hgDt9O5HfNN13wt408T6t4b1K6fRLF9JuvtBijeWYMfl5ztXOcfd4x/ePYA9Krgtf1OTV/ifpng9pXj09bF9RvEjcqbj5tqRkjnaCNxHfoeK7xdwRQ5BbHJAwCfpXDeL/AAdq154q0vxb4auraHWLGMwSQ3e4RXEJydpK5IPzN27jpigDn9X0m00r49eCxZIYYZLa7PkKx8tCIn5Vei54yBxx9at2N7eeMtb8S/aNJGo2VhqL6dBC98YViEYG5toHLMxJ3dRwBjFWbrwr4v1bx5oHie6m0a0OmxSo1vGZJgN6lTzhdxwx9MYHWmT+D/Fnh3xlqet+D7rTJbPVnEt3YaiXULL3dSoPUkn8cYPGADLkv/F3w8+GWqDVbmOS5F2sGlzvN5zRRyMAN5I52DJGc+nSuq1jwAt1Y2Z0nVbrTtWtpklOpFmllmA+8smWG4NnoePbHFGqeCrrxR4T1LTfEWoI99fqvz2yEQ2xQ5QRqTkgHkknLZPQYApaDpPxHS2j0jW9V0f7BEoja+tRIbuVB2GcKrEcbsZHXk80AMfU38R/F+88NXUjjTNK09bg2ysVW4lcr8z4+8oVgNp4zzVS7muPBXxY0LT7CWX+xNfjljeyZiyQzRjO+MH7oOVBA46+2NrWPB97H43tPF+gS263yQfZby1uXZY7mHt84BKsMDsQcD05sxeHLvVPGFn4k1pYIm06F4rG0hkMgRn4eRnIGSRwABgdck9ADE8FSQeP21rXNWX7TCmoSWdlbsx8uCJAMMAP42JyW69MECuU8MatJ4O0D4oahbs80tjqsohaZi7FslVLE8tyQTnrXVaP4R8UeDNe1T/hHJNLu9D1K4Nz9mvZZIntpG67SqtuHT34HTGTHoXw41RLbxfZa7fWUtn4guJZj9mjYOjMThuTgY4O3np1oAo6p4Z8Sar4cs7jR7Q2uvKIpo9WbVCzSHgtvG3BVhn5eg4GMcUeOl1T/hMfh/G+pXdpNfzMl7Fb3DeUHVUyVU8ZBZsHHfNaPhzQ/iTothDoMmp6FJp1uoih1EpI1wsQ4ACcKWAxjJwO+6p/E/g/XdQ8R+FLzS/sDWmgyM5N5dyCWfcFBziMgH5Tzk5J7UAdP4c8MWXheC7hsbi9ljuZzORdXDS7CQAQpPOOO5J96yvijawXfwz18TxhxHaPKoJPDKMg/gea61C5jUyKqvgbgpyAfY4GfyrM8TaQdf8AC+qaQsgia8tZIVcjIUspAJ9s0Aecz6daWX7Ocs9vFskudCheVtxO47Ae546npWX4i8PxWPwS0/xPb32oJrVnp9lNBdC7ceWD5Y2KgO0KAcdM9ySc56UeFvGFz8K5PClyuipcCzWxidbiTaUXA3sdnXAA2ge+R0qxrXhLxFqnwih8IoulpfC1gtXma6k8sLHs+YfusknZ0wMZ6mgDH+IlzfXB+H95b6leWkuoanaRyrDMRGd21slPukg+oqXxnAPhzoEz6JqGoJc6/qcFs81zcGb7NuDbnQtnBIB655I9BVzxF4P8UaxZ+EI4Y9HSTQ7mC6l33kuJGjwNq4i6HHU+vSuk8ZeEovG/hSTSb5/ss5ZZYpoiX8mVejDpkckduCelAHP+P9ATQvBtzrnh+aex1XS0E6XCysxmVT8yy5J8wFc/ezzWNq2rPqfjH4WaxDvtm1eKWW5jjYhX/dRsAR3wWOCa6jUdF8UeI/DH/CPaudPtknRYr2/tp2dpEGN2yMoNrNjBySFycZqHxj4Kv7uXwxf+GWs4rrw9IRb210WETxFVUrkAkHCACgDP1cG1+PnhyO3d4ormwuHmjRyEkYB8MV6E+9TXN5/aXxluNA1xSdNGmCTTreQkRTyZG9sdGccgegBIp8nhLxVffEDRPFN3daTG1lbyQzW8YkZVDZGFJALHkncdo6DHUml4gTxD4g8X3r6Ha+H9b0/TlS2aDVEYrb3H3nCEcFsFMk9OAP4qAJPhlp13FrfibzdR1K60+x1F7bTzNeSSR7P4lCkkHbwM+ua5/wCGPhKw8S+BNSGozXjKNQuVhjiuXiSJuPnAUjc3u2cY4xznuPD2r+KIbsaPrPh3TbOQ2zy2bWd1+5bYVBRhtJT7684Iqr8P/C/iPwh4Wv8ATbpNLnuZLiS4gaK6kCEvj5WzFkYweQD9KAHfBzVrzWfhjpdzfzvPcIZIjLI2WYK5C5PfjA/Cu1vknlsLmO2nFvcNEyxTMu4RsQcMR3wecVyfwx8Mat4O8IR6JqpsneGV2jltZWcMGJPIZFxjPvW94l0qXXfDGp6VBcG2lvLZ4Vl/ulhjn29aAPJfFN0mgfD2w1HSrue/1eyu4vP12HIWd9+HUuxzKpyRgblGO1dB8U1uE8SeCfsuoXtqbrVo4JBDOwQjIwdhyhI7Ej86o3/gPxrq/wAMIfC11NokElisK25haRvPEZGNzEDZx6A5PpW54r8MeKfEV34Vu1XR0m0m8W9uVNzKquwIOxP3Z4wPvH8hQBlX9gnhH4xeFE0q4vFg1iO5jvoprqSYTFEyGO8n5skc+31r1auE8SeGPEGrePvDWvWqaYLXSBIXjlupA8hkUBsYjIGMcc8+1d3QBx/iW2ibxXpdxqeqK+miGRE0URNI11Mf49i/fCj1BC9eM5rC8AyvqOteOvD93FONMtrqMQ2c758lJFYsgwxwvHQHAzV7W/DHiZPiZbeK9Dl06eI2P2GW3vndfLG7duQqD3x+vrw7wr4V8RaF458Qald3GnXGn6u0UzyIHWQOqkbVTkAZY8ljwB3PABzHwg0PTrv4PvPPbB5JluY5CXb5l3HjGcdhTPh1c2/hj4DHxPBbhr+G1uiGLEhiJXCgjOMZC/lXT/Dzwlr/AIU0afw9fvp76UksphnidzNIr9AVIAXGc5yfTHeoPB3gfWtN8FXXg3XW099JKTwpPbO5lkWQk5IIAQgsT3/TJAMq88O+Ite8IWV1pto0OvvFDcRawdUO9mO1mJAGNpGfk6DI44p3xBGqpf8Aw+Nxf3Npf3WoQQXq2tw3lF/lyQh+U4bOCV+taHhvQPiR4esItAXUtBn0y3Hl29/KkrTxxdh5fCkgdATgepq14u8G61ql74VbSns5IdFvEu5JL65cSTsCCR8sZGTgkn1PSgDJv7BPCPxi8KJpVxeLBrEdzHfRTXUkwmKJkMd5PzZI59vrUOiaHp0vx88VRvbAqlpBOo3sPnIQluvPNdD4k8MeINW8feGtetU0wWukCQvHLdSB5DIoDYxGQMY4559qbL4W8QWPxQvfEukvp0lnqVmlvOLp3DQsuBuCqPm4UcZGc9R1oAzw1xbftCxWgvbyS1k0RrjyJZ2dEcyFSVUnA4UdKm8LXaeOPFPix9VzPaaZenT7WzYny0VchnK9CzEZyckYwMVbl8MeIX+LUXiwJpn2GOw+w+V9qk80ruLb8eVjOT0z+NSW/hLUvDfjTVNd0A21xZ6vh72wuJWiKyj/AJaRsFYHOWyCB169qAMrw9Pd6Z8Tdc8DTXV3PpEtkL+yLTv5lsCVDIsgO4LljjnjA9a53wJoEfij4YapcaxqGp3U0NxdeQzXkg8tlGQ/BG5s92z04xzn0XSfDd3a6/qvii9FtLrN7EsEUCSMIoIV6IH25OSNxbb16DjnF8FeEfEfhjwNqeiXC6VNdzyTSQPHcybMyADDZjyMc9Ac+1AGl8KNVu9a+GOiX1/M81y0bo8jnLNskZASe5wo5rsq5P4b+HdT8J+CrPQtUNo8toZAslrKzq4Z2fncq4I3Y79K6ygAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5bVZ0s/HmkzTMFjlgeLcTwDz/Uj866kEEZHIrO1XQtP1oRC+hMnlZ2YYrjOM9PoKfpekWejwPDZIyRs24hnLc/jQBU1HR5bzxBpeoI8YjtN/mBidxyOMcetbNFIwypAJBI6jtQBz/jedIPCl2GPMhVFHqdwP8ga27RDHZwRt1WNQfwFc7L4Jt7uZJL3VNRuSrBgJJAR+WK6igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigApCMggEgnuO1LRQBy2h+B7fQfEOp61b6xqk1xqbq92kxhKSFc7eBGCMZPQiupqGS8tYjKJLmFDCoeQM4GxT0J9BweajXUrBrH7ct7bG06+eJV8v/vrOKALVFRwTw3UCT28sc0TjKyRsGVh7Edai/tGx+2/Yvtlv9qxnyPNXf8A985zQBZopskkcMbSSuqIoyzMcAD3NV4ru21O0kawv43U5UTWzo+w+3UZ+oNAFqivOPhd4iuLjw5rN14g1nzWh1qe2S4vJFT5VVMKOgHc4GOpr0VHWRFdGDIwyrKcgj1FADqKqwanYXNy9vb31tLOn34o5VZl+oByKmeeGOWOJ5UWSTOxGYAtgZOB34oAkoqva31nfK7Wd1BcBG2sYZA+0+hx0NZumadPZ+ItXuZtdmvEu/LeKwkxttFAIO0dcE/Tp360AbVFVZtTsLa4FvPfW0U5GRG8qqxHrgnNTSTxQ7PNlRN7BE3MBuY9APU0AcpffDvTrjXLnV7DVNY0e7uyGuf7OuhGk7DuysrDP0rc0nQrXScukt1dXLLte5vJ2llYemT0HsoA9qtw39ncXMttDdwSXEX+siSQFk+oByKLbULK8kljtbu3neI4kWKQMUPoQDxQBYooooAKKK8nvNS1rw98adL0nUNdvZdB1WNmtEcqAsw/gZguSMgf99rQB6xRXkvxB1XV/Cfjrw7cNr17H4d1KfybqAMg8ps9QxXIX5gfbafUV2Xj2a8t/CrjTL2e11KWWK3s3jIy0ruFAOQcjkk+wJoA6iis+FoND0uFNQ1TeEAVrq8kVS7e54H4Cr0ciSxrJG6ujDKspyCPY0AOooqrPqVjbRySXF7bRJEwSRpJVUIx5AJJ4NAFqimJLHJEJUkVo2G4OpyCPXNQpqFlJNFCl3btLMm+NBICzr6gZ5HvQBZoqrcalYWkyQ3N7bQyv9xJJVVm+gJ5qy7qiF3YKqjJYnAAoAWiqv8AaenieKA31t50yhoo/NXc4PQqM5I+lWXdY0Z3YKqjJYnAAoAWiq9pqFlfhjZ3dvcBThjDIHwffBpLrULKxKC7vLe3MhwglkCbvpk80AWaKasiOgdHVkIyGByDUFrqFlfFxaXlvcGM4cQyq+0++DxQBZooqK5urezhM11PFBEvV5XCqPxNAEtFRW9zBdwrNbTxzRN0eNwyn8RUtABRUMl3bQs6yXESMieY4ZwCq/3j6D3pba6t7yBZ7WeKeFvuyROGU/QigCWiqz6jYxXi2cl5bpdP92FpVDn6LnNE2o2NtMIZ7y3ilOMI8qqfyJoAs0VVbU7Bbj7O19bCbdt8syruz6YznNSC8tjdG1FxD9oAyYt43+vTrQBNRRVa41Gxs5Uiuby3gkk+4ksqqW+gJ5oAs0VieJ7CbVtD2WuvS6NtkSY3sBX7gOSMnjB/zkcVryzxW8fmTSpGmQNzsAMnpyaAJKKrrf2bXrWS3cBu1G4wCQbwPXbnNEWoWU93JaxXdvJcxcyQpIC6fUZyKALFFRSXEEThJJo0cqX2swB2jqfoPWo7e8tdRt2eyvIpkOV82CRX2n68jNAFmivO/h9qOqXPjXxvp+oapc38VhcwR25n2jYpEhOAoCjt0A6Cu7uNRsbWeOC4vLeGaT7kckqqzfQE80AWaKKCQBknAFABRVRtV05PI339qvnnEOZlHmH0Xnn8KtMyopZiFUDJJOABQAtFV7S/s79Wazu4LhVOGMMgcA+hxWLpXjXRdZ1/U9Js7uJ5NPaON5PMGJJG3ZVPXbtGcdzjtQB0VFVI9U06Xf5d/avsUu22ZTtUdSeeBToNRsrosLe8t5So3N5cqtgepwaALNFQ293bXcZktriKZAcFo3DAH0yKjt9SsLyV4ra9tp5E++kUqsV+oB4oAtUUx5oo3jSSRFeQkIrMAWIGTj14BNeb/FfW7y08MWOqaDrssSNqEVtILR0KOCTnLAFgeOzD6UAel0VWuNRsbSQR3N5bwuRnbJKqnHrgmrCsGUMpBUjII6GgBaKZLLHBE0ssixxqMs7nAA9zUdpfWl/EZbO6guIwcF4ZA4z9RQBPRVefULK2WRri7giEWPMMkgXZnpnJ4z2p1xd21pbme5uIoYR1kkcKv5nigCaisTxFdPL4P1K80vUTC6WsksNzbFH5VSRjIYEZH/6qxfh34gW4+Hmg3es6pGb27jY+ZczAPK29umTz2oA7WisnxLp1xqvh+6s7XV5tIlcKRew43RAMCepHUAjqOtU/EfizTPB+hW93qF2JmlKRW43DdcOcDI7Y5yT0A/AUAdFRUUV1bzwGeGeKSEZzIjgrx15FMtL+zv1ZrO7guFU4YwyBwD6HFAFiiq93qFlYKrXl3b2yscKZpAgJ9smqWv8AiLTPDWhT6xqNyqWkSbgQQTIT0VfUntQBq0VlWPiHTb3S7W9N7axLPGr7WnX5SVB25z1GauTahZWzIs95bxM4yoeVVLD1GTzQBZopkk0URQSSIm9tq7mA3H0HqaZb3dtdo721xDMiMUZo3DBWHUHHQ0ATUVWtdQsr4uLS8t7gxnDiGVX2n3weKs0AFFFc9qfjPR9K8T6d4fnuYxfXodyC4AhRULbmJ6ZIAA78+lAHQ1x118OdPfWrvVdO1bWdHuLx/Mul066CRzP/AHmVlYZ9xisvUNV1KD42aJpyatcPpV1p8twbbKiMMAwBBUAkcA8k13tpf2d+rtZ3cFyqHaxhkDhT6HBoAq6VolrpKs0b3FxcOAslzdTNLK4HQbm6D2GB7VpVWutQsrHZ9rvLe33nCedKqbj7ZPNWQQQCDkGgAorG8VRatP4buodDmaDUpCiQzAA+Xl1BbB44Uk/hXmvxPfxB4H8N6beWHi7WJ7i4v47aUz+SV2lHJIAjGDlR3NAHsdFcdP4e1/T9Y0q7sfEuq3tqtyFvLW78plaIgjcCqKRg49a6htRskZFe8t1Z5PKUGVRuf+6OeT7daALNFVbnU7CzmSG6vraCV/uJLKqlvoCeanlljgiaWWRY41GWdzgAe5oAfRVb+0LL7Wlp9st/tLruWHzV3svqFzkin3N3bWUJmuriKCIHBeVwq5+poAmoqsdRsham5N5b/Zwceb5g2/nnFebeFry5+IegW+sx+JrvS9UkvpJY4befKrAjkCIw52sNo+8RnJzkjigD1KiqtxqVhaTJDc3ttDK/3EklVWb6AnmrJYKpZiAAMkntQAtFV0v7OWaOGO7geWWPzY0WQFnT+8Bnke9Mm1TT7eAzz39rFCH8syPMqru/u5J6+1AFuiq1zqNjZpG91eW8CSHCNLKqhj7ZPNWaACiq1tqNjeu6Wl5bzvHw6xSqxX64PFPmu7a3LCa4ijKoZG3uBhB1Y57e9AE1FMiljniSWGRJI3G5XRgQw9QR1p9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBj+Itfh0CxWVk82eU7Yos43H1PsK4508ba5+9xPbxN0VXEIA+mcn8c10OoJE/xA04XIBQWrGEN08zcf1xXUUAeYHwX4mf5muEz/ALVwSacvhzxjYndbzynHaO6/oSK9NooA4fR/FepWeoRad4ht2jaQ7UnZNpz79iPcV3Fc743S2bwvcNOF3qVMRPUPkdPwz+FbtsXNpCZM+ZsXdn1xzQBLRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB5D4X8PaRP8AGXxzZzadbvZxLaSJbMgMO4xglin3SckkEjjJx1pPh94e0mfxt4606ewgn0+x1BGtbSVA0MLSBtxWM/KD8oGccAYGBmuu0TwbqGk+Nta8SSaxbTnVVjWWAWLIE2LtXDeae3XI59qTw14M1Hw/4k1/V21m2uf7ZkEskX2Fk8tlDbcHzTx83OeuOooA4DRNXufC3gb4mLpeYk0zVrhLJV6QBmCjaOwXr+Fb174Dv/E/gjT7O2n0e1+SG5tr+CCTz1bht+/dks3OT3zmtnQvh2+np4mt9U1ODUbPxBPJPcQrZmEoz5ztbzG4544zwOaq6H8PvEmh266TD45uToUeRHALNBcLH/cExJK/UDjtigCJ7qTU/ivpPhzV50ubex0X7btx+7ubreELlehwAxA7HJ7V1Nr4R06w8ZXXia2kmhuby2W3mgRgIZCCCHK4+/gYznpn1rI8VfDmHW5tKv8ARtRl0TVtKTyrS6hTeBH/AHGUkZHXv3Oc5rT0Tw/q1mGu9Y186rqgjMcMr2yxQwg9cRoRknAySc4GBgZoA4P4UeGtF1vRfEUmq6db3p/tu7iUXCbwikJnaDwpOeSME4HPApPh2YLr4E31pqerS2FlE9zbm8V8NDHu7H8SMe+BXXeC/BmpeDtN1O0TWra7N7dPdq7WDJ5cr7QcgSncvy9OD71kWPwokg+HGpeDbvXBcQXcvnR3Edp5bRvuV+RvIYZUccd6AMXxXN9ltPh3LpthJaWdvrFpb21xMQk0kTLg/IBwrKMnJB9Vq34s0fT7r48eFY5bVCt3ZXP2kKNvnBUfAfH3h2IPUcHjitHU/hzr2s6JpNvfeLQ9/pV1DcW0q2KiMGPgbl3ZZunOQOOnOa077wTqd7430bxM2uweZpkDQiFrAnzd6kOSRIMZzxgccdaAMDTLC00L9oG5sdLtorOzu9BE01vAgSMuJcBto4BwP1PrS+B7O3sPjR49t7WFYoVjtGCKMAFowx/Uk10R8GX5+JP/AAl41i3x9k+xi0NkeIt277/mfez3xj2pND8GajpPjnWPEsus29x/aoRZrdbEptCABdreaew5yDQBxGo6jJ4Qm1/RPGukyXXhvW7uWVNat08wIJOiyDqCvAB6jaMAgVf+Jlpp994m+Ht3EEf7VqKR/aYW2tJEdpHzrg4weOe/FdVN4S1afw3f+H59YtZrG8My+Y9mfNijkYkqPnwSNxCsRxgcHFV9c+H02ozeFxp+qw2Vp4deN7aGS0MxfYFADN5i8YXsO9AHMeM/D2k6b8UPAsWn2MNlFfm6t7pLRfJE0aqh2ttxkfMc565q3fadY+H/AI6+Fo9Is7exjvrC5iuI7aMRq4VWZchQB1A/IV0XiPwZqOveKtB1xNZtrY6MzvFCbEv5hcKH3HzRx8vHHHvS6t4N1DUvHmkeJ01m2hOmRvHHbGxLBw6lWy3mj14wOPegDsaKxLbSNUh8X3uqy65LLpk1usUOmGIBYXGMuGzznB7fxewrboAK8z+N+izXvgqPWrLK6hodwl5E6/eC5AbH0+Vv+AV6ZUN5aQ39lcWdygeC4jaKRD/ErDBH5GgDxrVLRfjJ4X1bWIIji00+OLT0x0usLNNj8o4s+zVpfDrxI/xA/wCEeeUs/wDYVoZbwn+K6O6KMn1OwSP9XFd74P8ADVv4Q8LWOiW7+Ytup3SbcGRySWb8zUPhPwhY+EY9USyxtv76S7PGNgbGEHsMcfWgDkdEn1LxL418XSBdMmNhdrZRxX8TSeVEF/hAOAGO4n1x7DG94A8JXvg6DUrK41GC4tbi5Nza20SFVtgxO5VyT8v3cfj61T1f4e3/APwl0/ibwv4hfRb27QJeRNbLPFPjAB2kjBwPf8MnPT6Ho0ulxyzXuoy6lqM+PPu5UCZAztVUXhFGTgDuSSSTQBqyRpNE8Uih43UqykZBB6ivHPhf4T0HVH8YR6hpVtdxQa7cwwxTpvSJRj7qnhTjAyOeBzxXsteQfDO31p73xo+l6hZwofEFyrR3Vq0oBz94FXU9+Qc5wOnOQDF03TltPB/xS8MyBp9L0cyS6eJGLeUTG7YB9tq/iT6muo8JeH9I074baR4tTTUn1uw0driG4YkuT5J+XryMcAHoOmK6CTwL5fg3VNDstRCXWrGVr+/uLfzWleQYdtoZQPQDOAB0NanhXQbjw/4ZtNEu7yG+jtYRAjrbGLcgGMMC7Z4+lAHM/DLT7DX/AIYW11qcEV/NqvmyX8s6hmmcuyncfYAAegAxXAZmuvgt410jUS14vh/UJLWyuJjuZVV1wAfbn8CB0r0/RPBmoeE4rux8PatBFpc8rSxW93amVrVm67GDrlf9kjj1POcfxx4es/DHwV1+wsy77ozNNNKQXmlaRSzsR1JP9BQBna54E0C6+DP9oLYQxanb6Ql6l+B+/wDMSIPzJ1IOMYJwB0xgVTi1691/VvhjpOslns7+ya8u1f7t1KkRMe/1GVDY6EsK6jTPDmra94D0rSr/AFiH+yJ7GATLDalLiSPYp8svvKgHoSFyR6da2PE3gix1+10z7PK2nX2kyLJp11CgPkEY+XaeGTgZXjoKAOV+KsP/AAjt54c8VaQgg1KPUorOXyht+0QuDlGA+8Pl4z0yaTxJqOreCPG2q6/e6O+seGtSghimkgAeSxVFIKlT1Qksx6Dnr69bN4YutY1HTrrxDeW91Hp0v2i3tra3MUbTAYEj7mYkjJwBjBPepl0fWbXVdWurTVLV7bUJFkW2ubVm8hhEkZwwcbgdgOMD60AcJrerWT6L4E0fw3dQyaJql4Y1aYHy3RB8sLgYOCxAK99uD3rZuPBGrP4z0jxKl7pWnNY5juEs4GQXMJxlGy2OOcfX2FSXfwp0q58B2XhmK7ngkspTc219GAJI5ySxcAdssflB6Y5yM1e0bwrr6SwHxL4qfV4bdg8UEdolurOpyrSEEl8HBA4GQCc4oA7CuN8Rx6YvjnQbue4u7nU4YpvselQBWD5GGlbdgLgZ+Ykeg54PZVxXiHwRqGoeN7HxVo2uDTr2C2NpKktt56SR5J6bhg5Y/kPxAMj4fy3CfE3x/aSxRwIJbSb7PE5ZEdozuIOByeCeOvrXplcVoXge+0Pxvqevrr73EGpJF9pgltl3u6LgHeMADknAXvjtXa0AeQ6T4f0m6+PHim1n0+CS1FjBMbdkHlM5CfMyfdY5JPIPPPWqvh28bwpc/FlNKjWK30zbc2tug+SJ2jkJIXoBlV49BXb6d4Nv7H4h6l4rbWLeQX8KwPaiyK7VULtw/mHn5eeO/QVFofgO403XvEt/qGp21/beIMC6tRZGMAAMoAbzG4w5ByOfagDm7bwbf+LvhlYWol0dftttDci+MDtcCUgOZC+7l85yfcij4h6Pd6P4b8NeK5JE1DVPDM0X2udV5uIshZM++QD7ZY1p6J8OvEXhxDpmleOLmHQd5Mds1lG80Sk5KrIxOPrt/Cu2k0Sxk8PyaI0RNjJA1u6sxYsrAgkk8knJJJ5J5oA8r8W6paWfxM0LxjYQRyada+TZ6jeDGCLhSUPT+FSrE+joK7/RIYtT8Uat4g8tSExplrJjkpExMjZ95Cy/9sxVC1+HNjb/AAwl8GNMZFlgKvcsvJl6h8Z7ELgZ6KBXT6NpcGiaLZaXbZMVrCsSserYHLH3JyT7mgC9Xkut6nf+BvFOvXmvaK+reFtadS97AvmNbKECGORf7g/Ac8ZJIr1quZGgazDHq1vFqtpLaX8ssiR3FmWNuH+8ow4DjqcEDknntQBwHxZtdHuPgfZ3WnOl3b2yWyWdyeW2ZC5+pHUetaHxusreXw3oV00Si5TV7aNZ1GJFUhsgMOQOh+orW134ZDUvh7Z+DNP1VbKwtwm6WS286Ryp3Z4dQMscnirfjDwVqfi/RdN0+bW7W2a0uY7p5UsGbzHTIGAZflHPIyfrQByvxO8P6VpWpeDLjTbOOxuJdaitpJ7X91K8cnDhnXDEkZ5Jzyeean8Y6Tpvh34heALnR7C2sJZb2S3lNtEI/MRlUENgc9T19TXS+MPBmo+K30VxrNtaNpd2l4MWLP5kq9P+WowvtyfejxR4M1HxHrWgakus21qdHn+0LH9hZxK/y5yfNGBx0689TQByl7oGlXv7Q5trmyikt5tD8+aEj5Jn80jLr0YcDg8ZAqSLSLfwh8d9Lt9ChW00/WbCU3dpCNsYaMMQ4XoOQo49T6ml1KG/l/aFgFjdwwXK+HgxaWEvG/705BXcDjvwcjA69D22m+GpYdduPEGp3cd3q8kH2aJkhKRW8QOdqKWJOTySTk+woA47wTcG08dfFC5VN5huIZAv97CSHFU/DGiaj43+GPmXDaNO2sRytPdTwO8wkLMN27PVSMKOgCgV1vhjwXfaB4m13V59XtrxNZlWSeAWJj2FQwAVvMbj5ucg5x2rJ0z4ba34avLmLwx4wk07RriUy/YpbJJzCT12Mx4/L0zmgDrfCWmXuieGLLStR1Eahd2iGJ7gAgsAcqCCSchSo/DNM8a2lveeCNciuYY5kFjMwV1BAYIxBHuD3rS0zTotKsI7SF5JNuWeWVtzyOTlmY9ySSf5YFRa9p0+r6Dfabb3Mds91A8JmeIyBQykE7Qy5OD60AeNxeG9HuP2b/7Sm0+GW/XTzKl1Iu6VCrnaFY8qB6DA5Pqa2b3WNQ1EfDLSGlgZdUsftVx9rBaO4kS3VlVwCCRuYtjudtdDH4Av4/hgfBY1yAqYjB9r+wHPlliSNvm9ecZz+FM1T4af2t4Q0TSZtXaHU9DVBYanbQbGj2KFGULHOQqk4YcgdOlACQ+CtVg+IFn4p+3abZIkJtru2tIGRbpTnbuycZBIwfYVl/DmxtP+Fi/ED/RYP3N/B5X7sfu+H+76fhXUaJ4a1yO4huPEviVtYa3O6GGO1S3iD9N7Bcl2HbPAPOMgEV7DwTf6T441bXNP1pYbHVnjlurQ2oZ9yD+FycAHJz8p6/QgA5+0itvBPxqv4ZEih0zxPaG4R2ACrPFkuufQgsx92FY/gHXIfCGp+KLS4sfstnexHXtLgAw8kLkqEHHU/IAvbJr0Txn4KsfGlvp0N47R/Y7tZ9yjl06PH7BhwfoKXXvBdhr3iLQNYnO2TSJWcIF4lGMqp/3XVWH0PrQBw/i+KXw14d8H+HHIih1fV4k1Z4flEm9w0q5HRWZj/wABGOlavxe0uCw8DNr2mIllqeiyRS2k8ChGQF1Qpx/CQ33ehwK6zxd4U0/xjobaZfmSPDiWGeI4eGQdGU/mPoTWfeeFNS16wt9M8R6rb3mnxOjzR29oYWuyhBUSEuwC5AJCgZI6gcUAcPr1ja6x8XPAFxdWojfUrCeS7Qcb/wBwx2t6jkqR3HFT/FfQNL8N/Du2tNGsY7WB9ahmMUZO0u2cnnp0HA4FdXrXgvUdU8c6P4lg1m2tv7KV0gtmsC4YOpVtzeaOx4wBj3qXx54OvPGuk22nR6tDYRRTpcMxtDKzOucY/eKAOenP1oAzvBmpWXjDVby+1i2CeIdKneD7BOAfsCk8FP7xYAZk74wMDFd/XD6v4Ev73xZp3ifTdbt9N1W3iMN06WJdLxc8K6+aOAOOpPTngY7WISrCgmdHlAG9kUqpPcgEnA/E0Acn45h0qWXQX1S9uVaHUFltrC3UOb2UfdQqeoB5ycAdyOtc9ps1z/wv28hmtVtI7vw+JJYFk3byJQFZ8ADcBkcZwO5roPGngu68S6lomrabq39m6lpErvDI0PmowfG4Fcj+6P1/CovgPVo/HNr4pXxLuuhZ/ZLpZLNSJF3bsIARsGcep46nNAHJ+CvCGg6z4u+IOn6jp8dxZR6gix27EhEzv5XB4bsCOQMgYya6LwrJBrHxG8Tw3UasmhC3s9PtnGVt4yh3MoPdiPvdcACtPwr4M1Hw54j1zVZdZt7tNYnE80K2Jj2MN2Nrea3HzdwelVfEPw8u7rxX/wAJR4a1+TQ9VkjEVyRAJo7hRgDcpIGcAevQdDzQBPP4T0/wzoHjGfT5ZwmpwT3L2zMPLiby2zsUDjJPP0HpXDaT4W0S7/Z1l1C506Ca+GlTzLcypukRk3ldrHlQCOgwOvqa9H/4Ra+bw9qNpNrZudV1CAwT6hcWwIClSMJErKFA3EgZ6kk5rPsvAuoWXw0k8GrrduytBJbC7NiciJ924bfN5b5uDn8DQByPitE1X9mm1vr1RNdRadaukr8sG3RgnPqR1qT4oQxP8MPCkjRIzi8sVDFQTgocjPpXR3nw+1C8+GUXgo67brEsaQtdCwO4xoysoC+bgH5eTk59BVnxD4EuPEXgO18PXGqol3ZtC8F5HbFQGjGAShc9Rnv3oAzfG08kfjHwh4aso7OGzvZLieSKaPEMzxplFYLjIyc47kLViDwVqsHxAsvFP27TbJEhNvd21nAyLdKc7d2TjcCRg+wqx4j8A3HirRbJNS1t49csZ/tFrqVrbiMQvxkKmc7eAeWzkZyBxVzRPDWuR3ENx4l8StrDW53Qwx2qW8QfpvYLkuw7Z4B5xkAgAxfhpJb+KtL1nXNTijub26v57eQSqG8mJcBYRnooU5x3LEms3xz4UsPCnwN1nSrSWe5gt8SQm6YO0W6VTtU4GAM/qfWtJ/htqml+JL3VPCXimTRoNQk827s3tFuIy56soYgD8vxxxW/qngyDVPBWoeHZr2d3vkJmvZQGkeXghyBgcFV4GAAABgCgDjPiNZ2sP7Pc7RW0MZ+y2T5RAPm8yIZ+uCefej4vRo3wr0h2RS6XVptYjkcdq6VvA93qfgC48La9rIu1lt44ElgthEIxHgo2Mks2VUnkA44A5JytT+Gut6/4Wt9F1rxaZhbSRNC8NiqABOhYbiWbHHUAZzgnBoAofEzS7S4+JHgFmj2vc3c0czoSrOgCcEj6kfiak8X6ZYeGL3w9oGg2VpYWmuaqXu0IbyZWVRtjZQfusdvyjAOMY5Od7xD4J1TXde8P6oNfgibRZDLGr2G8zM23duIkUY+XgADGe9X/ABn4Mt/GmhRWN3dPbXdvIs9veW64aGUD7wGentn05yM0AYlx4I1Z/GekeJUvdK05rHMdwlnAyC5hOMo2Wxxzj6+wr0KuP0bwrr6SwHxL4qfV4bdg8UEdolurOpyrSEEl8HBA4GQCc4ptpZa5F8T7u5XXZrvR5LYmWweLEdo+E2BWzgsfmbAAOOvVcgHZV5b4ms7WX49+ElktoXEtjcmQMgO8hGxn16CvUq5DxR4Nu9Y8UaL4i0vVY7DUNMEkf72385JEcYII3Lzye/egDmPE+m2mo/HXw1ZXUKyWp0mYND0VgC/ykDqvqOh6Hin2FhaaD+0G1npVtFZ2t5oImmggQIhcSkBto4zhR+Z9a3rnwTqdx480zxQdehL2FsbYQvYZMqtncWYSAZO7soAwOKkfwbqD/EqPxedYt8Ja/YxZ/Yj/AKrcW+/5n3snrjHtQBy3g2LUfGmhavqE8ej3Jvr24hnF7C8kkag7VjyDwqrjA989STXZeAfD1/4U8LxaJqGqLqMls58qQKQUjPIU5JPBzj2wO1YY+HGraP4hv9R8J+KW0m11GUzXNlLZrcRhz1ZMkY+n64wK7TR9KXSbMxG5muriRzJcXM5G+ZyACxxgDgAAAAAACgDQryT9oP8A5E/Rv+wxF/6Lkr1uuG+IngK+8e2trZjW4dPtLacXCqLIyuXCkDLeYBjk8YoA7S6tYL22ktrmJZYZBtdGGQwrx34OeEdC1nwJb3ep2CXU9tqMj27yMcwlWBG3B4GeSO/fOBXrUcWpjTTHJeWjX2OJ1tWEefXy/MJ/8ernvh/4Ou/BGivpUmrRahbmV5kYWhhdWbGcnewI4PYdaAOemt9OTS/HUOlebrUl09w9/c3LKIYG8v8A1AfBLFBjCgEDgErXNeIFXUv2WrW9vEWe6gt4PKmkG5k/fqnBPI+Xiuv0f4a6jo8es6ZB4mkXQtSlllNstqvnKZBhh5hJ4xgdM+mDzUR+GOqTfDE+Cp/EkJgJVRMNP5WNZBIAB5g5yOSSeOMdyAYXxJ0PTNH8D+H9asLOKDVIb+0kF6o/fOSOdz/eboOp7Cun8fJ4g0zxBo/ijSNO/ti206OWO509T+8AfH7yMc5bjHAJx7E4seK/Amo+KfCmnaJJrlvbm0kjkedbAt5jICFwvm/KOeeT+FbU+k6x/bcOqW2q2ysLX7PPbvasY5iGLBh8+VIycfe6n8ADJ8Faj4Y8TaJqd5o0Q8q5ummu7OeIBoJiiggr0525zzkk81y3wkaLTfgmdYhtIXvbaK7lRvLG5ipcgZ69sV22l+FJtI/tu7try3XVdXm86ac2xMSELtUCMODxycluSSfaofAfg248F+HBoc+pw6jaKzlP9EMTfMckH52BHJ7d6AMn4ZafYa/8MLa61OCK/m1XzZL+WdQzTOXZTuPsAAPQAYribIzXfwc8c6NqTNeR6Bd3FtZzyncwVPujPfH8iB0r0bRPBmoeE4rux8PatBFpc8rSxW93amVrVm67GDrlf9kjj1POVu/AMY8CXfhfS74Wwvd5ury4g86SVnOXc4ZfmPr0AwMUAUfBPgnw5c+E/DWrXOlQXF+dJhDTzAsWV4lBVs8MAOACDgcDFcr8KfDOjar8K71tQ06C6fzrqJGnXeYl/wBjOdnr8uOea9O0DR9Q0PwtaaQdRtp5rO3S3guPsjKu1AFG5PMOTgdmFY3hHwPqHhLwld6FFrVvcGZ5HjnexK7C/XKiX5vzFAHE+CdB0vWfgJJfanZQ3l4bG6RLi4XzHiWMuECE5KBdowFxzz1pseuX8vww+Hdh5yAapdxWs7zklHjQkLG2MEqxCgjuAR3rtfD/AIE1Dw/8Pp/Ccet28yPHLHFctYkFFk3Fsr5vzHLHHI/Gqv8Awq2K5+HNr4R1HVPONk/mWd9Bb+VJC4LENgu2fvEdRx780AJfeB9WuPFWi+IY7zSdMfTGIm+x27J9ohJG6NstjGAcemawbTw3pGofHnxHY3VlHLZvpUcklu33HYlOWHf1+vPUA11+j+E/EKPCniTxbJq9rbsrJbx2iQCRlOVMrAkvg4OOASOc0ll4M1K0+Il74sOtWz/a7dbZ7X7AwAjBUjDeb975euMc9KAOo0vS7LRdMt9N06AQWlumyKMEnaPqSSfxq3XG31lrg+JunXdnrszacYSLvSzF+7jj2tiTdn7xfaAMZOG5wCK7KgAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAxfEegJrtmgWTybqE7oZfQ+h9uB+VclJqHjXRf3c0clxGvRzEJQR/vDn869HooA8wPj7XkO1ra2B94WH9aeni7xVenbbWgJPeK2J/nmvTKKAOH0vw1q+qX0V/4juGZIjuS3Zgcn3A4A/nXcVmah4h0vS7mO2vLtY5XGQu0nA9TgcfjWkCGAIIIPIIoAWiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiikZQylWAIIwQe9AEcV1bzSyxRTxSSRECRFcEoT0yO1S14x4P1/SvCHijx5EbS5cf2mDFaadaNKwUA5O1RhVGepwOa9J0fxnoGueHJdfstQQ6dAGM8jgqYdoywYHkEDmgDeornYPGmmSz6Yksd1bRarxYTzxBUuCRuAHJKkjkBgpNXbrXreDUn06CC4vLyKITzRW6gmJCSFLFiBzg4AJJweKANWisqPxHpkmgxa0ZmjspcbC8bB2JbaFCY3FieAuM54xVSz8Y6bc6+uhzx3djqUkZlhhvIdnnIOpQ5IOMHIzn2oA6Coobq3uGlWCeKVom2SBHDFG64OOhrJvvENit9caVHa3GozxRg3UNtEHESsOA+SBkjPy8kjtXE/Bc2pj8YGxjWO0Ovz+Qix7AsfG0BSBt4xxgYoA9Roqhqmr2ukRQNclzJcTCCCKNcvLIckKo+gJycAAEkimadrUOo3l3ZiC5t7q02GaKdACA+SpBBIYcHkEigDSqOe4htYWmuJo4YkGWeRgqj6k1z0/jnSorGbUY4r240uBmWW/t4C8K7ThiCOWUHOWUEDB54rnPi/8A2ZrPwf1HU4ltrtBHFLa3IAbaGlTlG7ZHHFAHo6OsiK6MGRhlWU5BHqKWsW31S10rw3pcl07AywxRxRopZ5XKZCqo5J4J+gJPANRaZ4s0zV9XudEZLi11KGPzHs7yLY7RnjcvUMv0J96ANmO7tpp5YIriJ5ocebGrgsmc4yO2cHr6VNXl3w9gg034h/EeKztVSGKe1ZIIECj7khIUcDk5rsvDHi7TvFttdXGlx3JitpjBIZo/LIkABK4JzkZHbFAG/RWboutW+u2stzbRTpHHM8BMybcsjFWxycgMCM9OKdqut2Oji3F1IxmuZPKt4I1LyTP1wqjk4HJPQDkkUAaFFYdj4r0681ptFlWey1QR+aLW7TY0if3kIJVxwehOKzj8RtBbUNR063GoXN/YbRLaw2MplYtnhUIBOMZJxgZHPIoA62isjw34l0zxXpP9o6XK7RCRonSRCjxuvVWU9CMj86n1nW7DQLD7ZqM/lRNIsSAKWaR2OFRQOSSe1AGhRWNZeJbO71p9GliuLPUlgFyLa4UAvETjcpUlSM8EZyPSud8MePbjXvFGv2MmlXsVtYTpbxAQ5KkBizSc8EkDA9AO+aAO5kjWWJ42LBWBBKsVP4Ecj6isPQvBuh+Gri4n0m3uIHuXMk268mkEjnqxV3IJ98ZrE8D+OrnxVqOsxTaZdW8dtqD2sOYxiJUQZEjA8MWzx2yB71paj480fTLdr2ZLx9Njk8qXUIrcvBG27acsOSA3GQCM8ZoA6eiud1rxvoPh+TTVv7tkXUXVbaVYmMTBu/mY2AYOeucc0zTPHWkap4hbQ1jvrW+MZmhS9tHg8+MdWTcASPyPX0NAHS1ja1pWieKbefRdSYXKKFae1junjODyu8IwOOMjPHFc9YePLi9+JGq+Hv7LvFtbCKJQwiyzO5++3PypjGPxJ9Be0bUfCt1481iLTIT/AG/5CG/lMLoSowFHzYB4x0HIxzQBv6VpNpounxWNisq20ShY0kneXaoGAAXJIAA6VdrDuPFNlHdXdvaW95qElkQLr7HFvEJxnaTkZbHO1csMjjmmDxroL+F38SR3jS6WgJeWKF3KY6hlALKR3yBjvQBv0VxUnxS8NxWtheOb4WF5sAvvsj+REzjhXk6BvUDOO+K7WgAormbnx7oNp4hn0KSS6OowwmYwraSFnAIACDblyc5G3IwCc8VWsviRod+l2kUWo/b7WTy5NNNm5ugcZz5Yycf7XQdyKAOvorE8P+KtL8S6ZPfWUkkaW0rw3MdyhieB1+8rg9MVSuPH2jWb2b3S3lvY3sgjt7+W3K28jN935uoB7EgA9c4oA6igkAEk4A71nalrdppk8Fs4lmvLjcYbaBN8jhcbjjoAMjLEgcjnkVkx69ovi3TNb04R75rNWivbG7iw0ZwSNynIIOMggkcdaAOit7mC7hE1tNHNESQHjcMpIJB5HoQR+FS1418NPHOleGfhHo/2yG/mSATG5ltbR5Y7YGeTHmMBheCDjOcEHHIr06+8TaTp+kWmpzXQa2vWjS0MYLNcPJ9xUA5JP/66ANeisax8S2V7rFxpDx3FrqUEIuGtbhBuMROA6lSQwzxweDwayF+JWgTSahDZpqV5c2Mnly21vYyNLnv8hAIAxySAM4HegDrpJEijaSR1SNAWZmOAoHUk02CeG5gSe3lSWGRQySRsGVgehBHUVzv/AAlPhvXfA82syTGfQ54ZEnzC7ELgh1ZVBYYGc/n05o0bXPDlh4Es9U07NtoSRBbddjZwW2qoXkkluAOpJoA6aisq216GfU206W0vLW7EBuQk0YIaMEAkMpYE5I4zn2rGi+JGg3T38Vimo3tzYyeVLbW9jI024dfkIBAHTLYGeKAOuorjo/ib4fudFTVLBdQv4iheSK0tHkkgAJB8wDhOh6nnBIyK118V6PJ4bt9fjut+n3ITyWVCWkZ22qgXruLHGPWgDaorLstciu9Uk017W6tbyOETmOdBgoTgEMpKnnsDkd61KAOffwToUniI+IGguv7V27PtAv5wQv8AdAD4C+2MVtz3Vva+X9onii8xxGnmOF3MTgKM9ST2qDVNUstG0+W+1CdYbePG5iCSSTgAAckk4AA5JNeVfEe+tL7xN4DlfSrizvzrttskubcK7xbxkBgTxkj5SQRkcUAetvd20d1FavcRLcSgmOJnAdwOpA6nFTVyep6j4VX4g6Na3kJfxHslWycwv8ilCX+b7p4BHcjPvWtc+ILaHUZ7CC3ub26tolluI7ZAxhVs7c5IyTtOFGW46dKANaiuY1P4geHtI0TT9WvbmaO0vwv2dvs7/Nu6A8YU45+Yjoagi+JPh99fttIlN9ay3bbbSa6tHihuT/0zZgM54wehyMdRQB11FZWo+ILawuWtEgur27SMSvb2kXmOiHIBboBnBwCcnBxnBqhD478P3HhebxFHdStp0BZZ2W3kZ4WX7yugBKkd8jH4UAdJRXFSfFLw3Fa2F45vhYXmwC++yP5ETOOFeToG9QM474rpNS1m202e0tnWSa7vGZbe3hALybRljyQAAOpJA5HcigDQZlRGd2CqoySTgAUy3uILu3S4tpo5oZBuSSNgysPUEcGsSXxDpF5ourS30VxHa2jPa30U1uxZfkDMCqglhtcHIyMGs/SfEvhXRvh3a6zpyS23h2JGEOy3kcqodgSQASAWB5PrzQB19Fca/wAT/DsY06V/ty2d+UWO+No/2dXcZCNJ03djjOCDnGDXQ6trVnoy24uWdprqUQ28Ea7pJnxnCj6Akk4AHUigDRormLHx7ol7rcmiH7bb6tEyrJaTWkm5M4wSygrg5HO7Fb99drYWE928csiQoZGWJdzEDk4Hc+1AFiiuUi+IOjTeDj4riivX0gMQZVhywAO0ttznAII/+tzV+XxVZQ+F4vELwXZsZUSRAsYMhV8bTtBzzkcdeelAGs93bR3UVq9xEtxKCY4mcB3A6kDqcVNXI6hqfhU+PtEt723c+JCki2TNC+Y1KEv833TwCOpxn3qm3jy4/wCFoT+Gl0u8NrbWBmdki3NI5kQBgM8IBuGe5P0oA7Vbq3a5a2WeI3CrvaIONwX1I64qWvIdPv8ATvDfxs8XMLZx5tnbGO2srcvJK5UE7UUcnqSencmvQ/DXirTPFdnPPpzTK9tMYLiC4iMcsMg6qynp/n0NAG3RWP4k8T6V4T0ttR1eaSK2U43JEz89h8oOMkgc4FUbDx5oWoandWMcs8f2a0+2PcTQmOExAgFg54IBPUccHBODQB01Fc0/jnSYNQsbW8jvbNL9xHZ3FzbskUzHooJ5UnsGAzWlqGu2un3kViEmub6VDIlrbJufYDgsc4CrnjLEDPFAGnRXNW3jnR7zRdS1O3+0Ouls6X0HlYmtymS25T6AHpnODjNF1440m08IQ+KWW6fSZYxJ5qRZKKSACy5z1NAHS0VzmreOdB0SPTHv7qSIam0Ytswtht+MZbG1cZycnpWZN8VfDFrY6heXT39vFYzCGTzbGVSzEZXAxxkdM4oA7aiuas/Heh3mo6hZrNNELCAXMtxPC0cJi6blc8MPccHtmlPjfS4tWstPvIr2xe/bZZy3duY452/ugnoxyOGANAHSUVzN74+0Gx8QS6FLJdHUY4TN5CWkhZxkABBjLk54256H0NWfDXi7TPFIvFsRcRXFlIIrm1uoTFLCx6blPrg/kaAN2iuZ1Dx3pGnWdxfyJeS6ZbSeVPfwwFoY23bTz1YA8EqCAc+lal1r2n21paXAmM4vQDaJbqZHnyu75AOoxznoBySKANKisPRfFem63qF5psXn2+pWeDPZ3Uflyop6NjoVORyCf1rR1PU7LRtNuNR1G4S3tLdN8sr9FH9foOtAFuisG38W2Emp2Gn3EN1Zz6gjSWX2mMKJwoBOCCcHBzhsH2qrdeP9BtdeudDZ7x9St4vNa3S0kLvyAAgxlyc5+XIwCc4FAHUUjMqIWdgqgZJJwAKxPDXizTPFUFy9gZ45bSUw3NvcxGOWF/RlPT/9fpXPeMPEWj6v4K11RZT32npbTp9tFuJIBKqkAg9Thv41BAI6jFAHcwXEN1As9vNHNE/KyRsGVvoRUlcf8K/+SX+Hv+vQfzNb+p61aaXJbwy+ZLdXJIgtoULyS45JA7AZGScAZHPNAGjRWJovirTdcvL+xgaWG/sGC3VpcJskiyMgnqCCO4JFU5vHekQ6e+qCO9m0iNir6jDbl4Rg4LcfMVBz8wBXg80AdPRXP69410Pw7okGsXty72NwFMM1vE0qvu5X5lG0ZzxkjNVk+IGht4gtNIb7ZE96Stpcy2rpb3LDtHIRhvqODkYJyKAOporn9L8Y6dq/iO+0GCK7TULAA3KSxbQgPQ5zyDkYxnrViHxJYy3erQSLNbrpX/H3POoSJPlD/ezg/KQ3sCM4oA2KhuLu2s4xJc3EUCEhQ0rhQSegye9c9P480i0tra9vI7210y5ZVi1Ce3Kwnd90k9UB7FgByOa5b40WlpJpXh6++zwtcrrVrGs+wbwh3EqG64zzjpQB6hRWJfeKdP07xLp2g3S3CXeo7/sreXlJNoy3zA8Y96RPFent4sPhl0uYtS8g3Kq8fyvEDjcGBIxn8aANyoYbu2uJJo4LiKWSFtkqo4YxtjOGA6HHrXFeF/Htxr/inX7CXS7yC2sZ0t4sxZKYDFmkOeMnGB6Ad81f8J6h4Wvdb8Qjw9CUvFnRtScwvHvlO7HD4OeG6DHPvQB1lFc7ceM9Oiiu54IL29tbJ2S5ubSAyRxsv3h1y2O+0NjvT77xnoVh4XXxI94ZdJZQy3FvE8oIzjnaDjnjnGDwaAN+iuPb4l+H0udOR/tqW+oMkcF61q4tjIw4TzOme3GQCDk8HG9fa3b2eow6ckc1zfzRtMttABuEakAuSxCgZIHJ5J4zQBpUVR0nVrfWbI3VssqoJHiZZoyjK6MVYEH0INXqACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAorC17xE2j3Vpaw2L3k9zu2oj7SMY9jnv+Vaen3FzdWay3dmbSUk/ui4cgduRQBaormvFfikaCsdvbxrLeSjcA33UXpk+v0rlRrnjab97HFd7DyNlkCP8A0GgDpPEHgtNb1Vb1bww7gFkUpuzjuOeOK6eCFLe3jgjzsjQIufQDFcXofjS7N/Hp+t2/kySEKkpQoQT03A/zruKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDzb4bgf8Jv8QmwMnVFBP4NWDp8mm+HLr4qatf2n2jw79pjjNuFys0xBEiDsPnkUH0z7V6PL4M0Z9VudThS7s7u6INy9neSwCbHQsEYAn3xn3q5L4c0ebQJNCewhOmSoUe3AwGBOSSeuc85znPOc0AeY+LjdPF8OLmea3hjm1uyMFjaqBFDGRwAx5YgEDIwP9kVueKNA1w+KLnxN4J1KJdXgjS3v9NuR+6ulA3IM9mw3B4+o5zq/8Kt8IHSodObTXaGGVZY3a5lMiFc7Qr7tygZPAIH41rf8IppSXkl5brc21xLGkMj291JHuRBhQQGxwOhxkc80AeXeIPFcOteGPBOuT282lad/bZS/SNivkSjepbcOwbc2f613d54V8Px6to+s315fXN5bzhbBpbxpCWfHCjPIwMntgE9Aa25fDGiT+Hf7Al02B9K27fszAkdc5z13Z53ZznnOaoeHfAHhrwrcG40nTvKn2lFkkleVkU9VUuTtH0xnvQBzXwivfPk8YW922NUTXp5LhGPzBSFC/h8rAfSj4SlTe+OdpBH/AAkl0Rj0zXS6j4C8OaprZ1mexkj1Bl2yT21zJA0q+j7GG7t19BVrQ/CGgeG57qfSNMhtZrp2eV1ySSTkgZPAz2GBQBm+O/D6eJLSwtINUfTdXhuPtOnXKDOJUU5BHcYJz/XkHl7PX/E19pXi7w3rOnxReKbTSXMV3Z8rdoVcIV9DuJx7k8DGK9E1TQ7DWXtZLyOQyWkhlgkimeJo3IIyChB6Ej05pdP0Wy024uLmBHa6udvnTyyNJI4X7oJJ4AycAccn1oA5j4XXNlqHwn0XHlmBbQwTK3QFSVcN+RP415tbQXFv+y1qgmL+S0pa139fKNwmPwJ3H8a9fPgjQfMumitpoIrxy9zb29zJHDMx6lo1YLz34575qzrPhfR/EGkLpOo2jPp6gAW0U0kKYGMAhGXIGBgHpigDz3xTcx23if4btf3c9ppskE0RnilMflytEgXLDpnOPoT711o8LaDp3i7TdYmub2fWijW9s0100jFNrFhg/wAIBJyehI7kVo33g/QtT8PJoV/Y/atOjxsjnmd2TAwCHLbgQOMg9Kj8O+CtA8KrJ/ZFj5Mkq7HmeV5JCv8AdDMSQPYYFAHLeAf+So/Ef/r5s/8A0B6ytVvLj4c/EjUjaQl7PxRbtJZxgfKNRXgL7biwJ92HpXe2PhXQvDl9qGt2dvdJdXAMt0/2ueUzbQeqs5DEZOOO/FYGla7YfErWdLvbCzuRpujyvcvNdQbM3G0oiL64DMxx0ISgDsdE0uPRdEstNiYuttCsZc9XYDlj7k5J+tcF4juJLP47+EpLrIsZ7GeC3Zvuic7s49yNg/GvTKztY0LTNfs1tdUtEuIkcSJklWjcdGVlIZSPUEGgDh/iJavP4++Hz2Y/05dQkJK9fIAUy59sfzo8IAf8Lq+IJ/2LH/0VXa2Hh/T9Ou2vI1mmuynlfaLmd5pAnXaGckgd8DGTyc1VsfB2iabrV5rFpBcx396MXExvZmMgxgZBcjgdOOO2KAOU+E3F545UfdHiW6wOw5FbPxA1jTdMs9IgvdOgv7q81KGGwiuOI45yflkY84C57c8471q6D4S0bw1Ldy6TbzQtduZJ991LKJHPViHYjd79af4j8L6P4s05bHWbT7RCkglTDsjI46EMpBHU0AcPJHcw/HzRlu70XM7aLKW2xhFX5zwq8kDjuSfep/hwf+K5+IY7/wBpocfg1dCPh34XW+sL0aaRc2OfKl8+Tc2evmHdmTp/Fn06VZXwXoKeJZvEC2bLqExVpWEzhHZfusyZ2kjsSODz15oA4bwB9p/sT4kLZlheDWr/AMkL1D7fl/Wl+H+i6J4s+FlhFNqN/JbC2+z3lsL1lRGXhgV7DjP0INdxZ+C9BsPEF1rltZtHe3T+bKRM/ltJ/f8ALzt3cnnGeT6ms2b4V+C59Xk1N9Ej8+Vt8qLK6xSN1y0YbafpjHtQByfjODT7e0+GlvpRZtOi122jti7Ft0anCkE9QQOD3GK1PFPHxy8BY6mC+B9x5LV1ut+ENE8Qz2U2p2ssr2LiS28u6liETA5DAIwGR69abeeDtE1DXLTWrmC4fUbNdtvML2ZTGMYOAHxyOvHPfNAHKeHDj47+NAepsrQj/vhaNE/5L94o/wCwXb/+y11V74L0G/8AES69cWbHUBGImdZnVZFByA6ghXHsQeg9BS2ng3Q7LxDNr0EFyupzqFlna9nbeoxgEFyCOBxjtQByPwUuJD4b1iyvMjU7XV7gXit94uxB3HPryPwrAsLV4NE+MTwjGmu9yIQPu+aIn83HbqVH4V6hdeE9IudVl1RYp7a+mQJNPZ3MkDTKOgfYw3Y7E8jsakn8MaPceH20FrPZpjKUaCGV4twPUEqQTnJzk855zQB5d4pA/wCGWrbgcafYkf8AfyKvYbEk2FsSckxLz+ArCuPAXhy68NxeHp7Od9JiIKWxvZ8DGMDO/JAwMAnArds7SKws4rWDzPKiUKnmSNI2PdmJJ/E0Aeer/wAnGv8A9i3/AO1xSeGQB8dvHBAGfstn/wCi1rrh4Q0VfEp8RCC4/tUp5Zn+2Tfcznbt37duf4cY9qbZ+DtEsNeudctoLhNSul2zzm9mbzAAAAQXI4AGOOMcUAeTXX2r+wvjEtnu3i7yQv8AdOd//ju6uyi8PeFvFngK1vb/AFS+n0Z7dJnEt+3lx7RyDzwVORjtiun0rwboWi3t9d2NrKs1+S12ZLqWUTE55ZXYg9T271m6f8LfBul6oNQtNGRJRJ5qoZZGiV+u4RltoI7ccdsUAYOkarDa/G69tb53i+2aPbDTPtGVYoOWQZ/iLZJH+yfSu0nj0mK61f7NBbrqUtsHu5I4wHZQpCb2HsDgHsDUXibwX4f8YQxR65pyXJhJMUgZkdM+jKQce3SprDwpoul6FLo1jZm2spc+YsUzq7k9SZAd5OBjOc44oA858FKp/ZiuMgYOl6jn/vqapbHWdPsPhv8AD61udPt77Ub17aPThc/chmAwJSevy5HA5OQPcdva+BfD1l4cm8PW1rcR6VNkPbC9nwQc7gDvyAcnIBwc81DdfDvwxeeH7XQ5tPkawtJPMtlNzKXhb/ZctuA9s49qAOZgjuIf2hLdLm9+1THw2S5CBFX9+flVRyB35JPPWpvhwB/wnfxDP/USj/8AQWrol+HvhiPU7LUU00rd2alI5RPJuYE5+c7vnOe7ZqxpngzQ9HvL+7sILmG41A5upPt07GU88nLnB5OCORmgDgPh5/ySbxcP4Rd6gAPQbKs+G9GsfEPwI0PSL27ktPtUaJBcR/ejmEhKEfiB/wDWrsLHwD4c03R7zSbO0uYbG8ZmnhW/uMOWGG535GRwcde9SQ+CPD9voEehxWky6bFIsscP2yY+WytuBVi+5cNzwRzQBzHhXW/EumeMIPCvjCC3u7trWSSw1e3GPOjBXcrjseFz05A68Gk+Gv8AyOXxC/7C4/k1dta6BYWt818BNLdmLyRPPO8jpHnO1ST8ozjOOTgZzgVU0vwdomi3V/c6fBcwzX7F7p/ts7GRjn5jlzg8nkc0Acf8H1X/AIRfxKuBj+3LwY9sJWN4G0W18S/AbRdJur6SymmupPsdzH96KdZpHQj/AL5Pp7EHFejaV4J0HQ7K8s9Nt7m3gvGLzqt9OdzHGWyXyGOBkjBqK18AeG7HRo9ItrKeKxjnFxHEL2f93ICSGU78qckngjnmgDA8I674jtPF3/CK+MLW3n1FLRprPVbYfLcRBgGDDHBzj06dOhPf215a3qO9rcwzqjlGaJwwVh1Bx0I9Kp2Wh2Njfvfosst48YhM88rSOIwc7QWJwM84HU9c1keDfCFl4Vl1aSxtPsUF9OHW2EpkCBQRuJJOCxJOAcAYFAHPfFa9+w6t4ImuW26WutRtcu33VYfcLew+Y/hR8V2X+1PAWSM/8JJbEfTd/wDqrvNY0bTtf0yXTtVtI7q0l+/HIPyII5B9xzXPL8MPCPk20U2mPcpbOHhFzdSy7CMgAbmOF5+706cUAYvjD/ks/wAPfpff+iqPEmha9B4p1DxR4H1CJ9RQRwanpdyP3dztQMmD2ba645HXqOQepvvBWg6jq9pqtxazm9sxttpI7yaMQjGMKquFAxxwOanl8MabJqN3qCfa4Lq8Ci4eC7lj8wKoUZAbHAGMgA+9AHl/jbXrfxR8IfDurQWRs459WgDW56RsrurAeoyDXQ/Gqy+3+EdPt4FzqMmq26WWB83mkkcfhk/hWZ8Z49H0fwJo+kR/Zra3TUrfZa5A/dDduOOpHqffnrXe6X4e0MXFvqtq8l80akWs0149ysQPB8vcxA6YyOccZxQBxHh1Yr74peNdNv8AUb61vzPDNBHDctF5sPlgDAHXaMf99Vd1bQdC8PeCPHdro7TNNLYzz3peUyfvWic8k9GI5I9Cp7iuj8R+AfDXiu7hu9X04S3cI2pPHK8UgHoWQgkfXpk4q0fCOhf8I4/h9LHytLcEPBDK8e8HruZWDNnvk896APMvFIH/AAy1bcDjT7Ej/v5FXWeLfDtx4gXQp9H1g6b4l0+B7iykYbkdCEWRXHPByg79ehzWxceAvDl14bi8PT2c76TEQUtjez4GMYGd+SBgYBOBVqXwrpUr6fIVulm0+NorWZbyUOitjcC27LZ2j72elAHL+GfEuoazovijTtc0uOw1zTVKX3k8xzFojscfVVHHPGPoOb0T/k1mX/sHXP8A6NevT18MaUun3ln5U3l3rF7pxcSCWYkbTukDbjxxjOAOBxVKLwH4dg8NP4ditLhdIc/Nai+n24ySQDvyASSSAcHvQB5549wP2atKI4IstPI9jhK6rx/p14/iTwjrOmsk9/p93IqaezhWuUkUCTaTwCqqTk4HvnAOzeeAvDmoeHrbQbuznl0u2x5Vu17PgADABO/JAxwCSB2ri/iCPDo8SaRpPi6zuYvD9taPLb6kWmc/aC23y2lBLABRnk8nbk4HIB1OleH7qX4hX3i25tjZLLp6WEVs7q0jYfezvtJUdFAAJ4Hauvry/wAKeH/D0Ws2V94Lv9YkhSQ/aXeaVrVo9pyp8wYY5xjbkg8nFeoUAeLackmi6n4n+GMakLqdyJNPO3IS0nBMxHbCKrAZ6t9aueAp7q8S08C3u5pPDl9Ibt2HEkUTA235sysPaH3r1A6TYtrSawbdDqCW5thP3EZYNt/MZpbbSrGz1K91GC3VLu+2faJB1k2LtXP0FAHBeMP+Sz/D36X3/oqizOP2itRB4z4dXH/f5K6vUfBuiarrdtrN5b3D6ha58iZb2ZPKz12hXAGe/HNJqvgzQta1q31e+s2e+gj8lZEmdN8ec7HCkBlyTwcjmgDldAA/4X14uPGf7Ptf/QVpfh7x8RfiKo6fbrc49yj5rqrTwbodj4guNdt7e4TUrhdk05vJjvXAABUvtwMDAxxjinaR4Q0XQtUvNS063niu707rmR7uaTzTzgsHcgkZODjjNAHO/Gr/AJJFrv0g/wDR8dVfid50PwVnNsjbEhtfMSPg+UHTcB7Y/TNdn4g8N6X4o046frEElxaEgtCtxJErYII3bGGcEA81Pb6RZW2k/wBlrE0tl5flGK4kaYFMY2kuSSMcYNAHIal4c8K+IfDNvqmo6tqF5pK7buGSS+dgGx8pGP4uSMdcnHWqfhnVUh+M/jDT9Rfy7y6itJLFZTgvCsZ3Kv0LZIHfd6Vt6P8ADHwhoOpLf6fpASZH3xh5pJEjb+8qsxAPvjI7Vc8TeBfDfi94ZNa01Z5oBiOZXaN1Gc43KQSPY+tAFbxDFpUfhzxfHp8Ful49hM940MYBZzE23eQOWwOh5wQe4zwV9LGP2WEJdcHT4lBz1PmqMfXNesaXoemaLpa6Zp1lFBZgEGIDIbPUsTksT3JyTXPQfC3wfbWtzappJNvcZDRNcSsqAnJ2At8mfVcHHHSgDlPHiq/h34aqwDK2saeCDyCNla3xz/5JNqn/AF0g/wDRq10N94A8Naja2FtcWEnkaeQ1pFFdzRLEw6MAjj5h69at654U0fxJpiabq8E9zZoQfKN3KoYjpuKsC345oA5b4vPJa/DF5IVcW8U9s06xcHyhIuQPTnFWdU8NeF9W0W01bUdUv72whdLu1ka+dwX/AICnqxzgDqScV1qaTZLpR0t4TPZtGY2iuZGm3KRjBLkkj6msDRPhp4S8Pagt9pukiOdGLReZNJIsRPdVZiAffrQBhSc/tGwnH/Mtf+1zWVF9rX4gfFkacWF3/ZtuYNnXzPsx24x3zXoTeENFbxMPEZt5/wC1guwT/bJvuddu3ft2/wCzjFGneENF0rXbrWrO3nTULv8A4+JmvJn8303KzlTjtxx2oAwPhxe6Lrfwm0uIm2ls4rIW17FLgqrKuHDg9M8nnsc1ha9c2I+KPhGN7maw0e60qWCweBzAqyFlIUdMAqEAHuorpz8LPBZ1ttW/sOEXLvvZQ7iJmznJjzsP0xj2ra8ReF9F8V6eLHW7CO7gVtyAkqyH1VgQR+BoAy7TwzoOkeModTE93NrlzbtCGmuWkYwjBJIP8IO0Z9SB3p3xE1jSdD8EX95rVguoWeFQ2jYxMxYbQc9OcHPbFXvDvhLRPCsMkekWQhaXHmSu7SSPjoCzEnA9Ogq3rWiad4i0mfS9VtlubOcAPGxI6HIII5BBHUUAeceK472Lxz8OnvruFpXvJcW9sgWKIbF4X+JuuMk4PYDpV60A/wCGjL//ALF1f/RyVtH4Y+EmtLO3bTXb7JIJYpTcy+aCBgDzN27H+znA7CtGHwbodv4jPiCK2mTUynlGYXc2NnHybd+3bwPlxjigDzi2kuotd+MrWG4XK20LRbOobyJeRjv/AFrd8PXdndfs+K9qyeUmhSxSYPAdYmD5/wCBA11mleENE0XVrzVLC3njvb05uZHu5pPNPONwZyDjJxxxnis1fhj4RSW7aLTGiju9xmt47mVYWJGCfLDbM+nHGBjFADfhXz8LvD3/AF6j+ZrHOqR2Px+kt9SlEa3ejJFp7SHCk+ZlkX3JBP8AwEe1dvouhaX4d05LDSLKO0tV5CJnk+pJ5J+tU/E3g7QfF9tHBrmnpdCIkxPuZHQnrhlIP4dOBQBleNbe1OgeJ00mCIa9PpTmV4Y/3rIFIUMw5yRuCg9cHHSm+ArjT9T+EWjlzGbL+zBBPn7vyJskz+IbNdBoPh3SfDNh9i0izS2hLbmwSzO3qzMSWP1NUR4G0BDcLFaSw2105kuLSG5kS3lY9S0Qbbz3GMHvmgDxi6hu4f2WLVbwMN1yrwhxyIzPlfwPUexFegfFbjVPARHBHiS2A+ma7DXvCmi+JtMj03VrRprKMgrBHPJEnHTIRhnHbPSoNU8FaFrX2A6jBdTnT2D2pN9ODGwOQ2Q4y3+0cn3oA5Dx1Kngz4gaF42OUsLlTpeqFR0U/NG5x6Ecn0UCqnxD0/Uovgfqkwjdb68kW+vkH3lDyKzLx2Rdq/7qV6ZqWkWOr6eLG/gFxbB432OSclGDKSep5UfWrkkcc0TxSorxupVkYZDA9QR3FAHG+M7jT9V+D2rXeYzZz6S00Ppkx7o8f8C24rivFsN5b/CDwHDqAYXSahp4kD/eHyNgH3AwDXo9t4H0C0jjgitZfsccnmx2T3MjW6NnOREWK9eQMYB5AzU/iDwlo3ilYF1i3muEgcSRIt1LEquM4bCMBkZPPWgDkfHBC/Fz4csxwvmXoyfUxrxSXLo37R1kqsCV8OkMAeh85jj8jXXa74Q0TxJYW1nqtq86WriSCQzuJY29RIDuz+NQ2vgTw1Zana6lBpaC8tY/LhmaR2YDJOTk/M2T945PvQBzHw4P/Fc/EMd/7TQ4/BqqeEluH8Z/FJbM4ujLCISOz+VJt/Wu0/4QrQR4jn14Wbrf3G0yss7qjsowrFAdpYdjjg89eafpPg/RND1W71PT7e4ivLxt1zI15NJ5p5wWDOQSMnHHGaAOb+CtzFcfCzTYQMTW0k0FwhHKyeYxIPvhgfxrhdOtpLb4D+OAmRp7ahcmx9DCHQAr/s5DfjmvXJfBuive3d3HBPbSXhzdC0upYVnPq6owBPvjJ7mrGpeF9G1bQRod1Z40wKF+zQSPCm0dB8hHHt0oA83+I2B8EvD5HBVtPI9vlFdF4z8O6lqXiKDWPCurpZeJdOtQrQzDMVxbuzEI/plkbn27cEbN/wCA/DuqaJaaNe2lxNp9pjyIGvZ8LgYXkPk4HTJOO1XJvDGmzahHqH+lx3sduLZZ0vJQ5jBJ2t83zckn5smgDL+H/iSXxLot3JeaYNO1KzvZLS+gXlfPXBYqe4OR6/U9T1lU9N0uz0i1NtYw+XGztI5LFmd2OWZmJJZiepJzVygAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA5jUSifEDSWl4Vrd1jJ/v8/0NdPWJ4m0I63YIIZPKvIG3wSZxg+me2cD8hXKx+Nda0WT7JrFgJXXjcx2MffOCD9RQBsR2qT/ABLuHnQN5VoskIbsflGf1NdbXm0vja1k1611RbKVGjiaGVdwO9TyMfQ1dm+JcIU+Tprsf9uUD+QNAG142toJvDNzLKq+ZDtaJ+6tuA4+vSt21dpLSF3++yKW+uK4O0TWvGl3DJfp9n0mNg5RQQsmOwzy316DmvQaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAorh7jxzJfLqL6JcaMq2MrwKL+52tcun3goH3VzkBjnJB4xgmg3xTN18K5PGml6XHKYDsubWa4KGJ9wU4IU7vvA9uD68UAej0V5trXxA8Q6Fo+l+I7zRbJdCuWhWdRcMblBIPv4xtA9sk9MkZOE+I+q67aeMPBun2Fxbx2d5fZaNt2ZHTBAcj+DkHA7jPpQB2es+I7PQ73S7W6iuXk1K5FtCYYi4Vj3Y9h7/0BrXrivFPjHVfC954dtZdMtZxqt9FZyXCTkCNmYA4TGTwTg57UnijxvqOgeMNE0KDRUuk1UyCKb7RgkquSNuPl5I5yeMnFAHbUVxP/AAl+r6Rp8KeJdPsLPVby+a1soo7v9zIgXd5rOeVUDdnjPA45qtbfEJ7XxxY+HNTbTbhNRQta3mnTFlVxn926knB9DnuOPQA37DxlpWpeKrnw5bi7F/bQfaJRLbPEoXKjjcATncOgx710FeZ6d/ycXrH/AGAU/wDRiVraB4v1S98f6t4W1W2tLaWxiWeFo9xN1Gf4lyeMZXI55JHagDtqKw9H1W+1HXdZt3S3/s+xmW3imQNukk2qzA5OMLuAyOpz0xW5QAVl69r1p4c0yTUb9Lg2sQ3SvDEZNg9SBzj3rUrmPiN/yTbxJ/2Dpv8A0A0AR2HxA0vVLGO+sLDWrm1lBMcsWmTMrAHHB288itPQfEth4iF39iW5R7SXyZ4rm3eF0bAOCrAHoRXCfDPxbZ6b8MtFglsNaleKBstb6VcSofnY8OqFT+BrrfDWpN4g8Mtr+nWsNteX7OyidWAIR2RA4ByDtUA46H8qAOmorzPR/iNrGqwXWnmwsoPFEGoiz/sty5AXGWlLdkC7juxj5cclhWx4+8Y6l4I8PW2oLp1vfNJMkEjCUxhGbOCFwSw4/vCgDtKK858ReOvEvhCa21LXNAs18PTTLFJJbXTST227oZBtCn6Lkds9M72reL4LfXF0SzuLBLv7MLqSe8m2RRoThRjqzHBOMjgZz0BAOoorhfDHj6fXNV1vQpLS0fWdLAdfs1xmC6jOMMrYJXqMg5xke+M/wz498VeL9JtNT0rw1aC2N8YLoS3eGSMEAlOBuIHJPHoAecAHZ2PiOzv/ABFqWhxR3K3WnLG0zvEVjYOMja3f/PWteuO0Txjfan4/1nwzdaZDa/2bbpKJFnMhk34I/hGBg9OaoaX418Ra34j8RaFaaJYRXelPEgllvGMI3BjliE3HOBgBR3yRxkA9Aorzzw9418S+JYtT0+10bT4dZ0u6e2u5J7hhbAg4BTALtnB44AAznnFXfDvxChv/AAZqmu6zbrZSaRPNb30UbbwHjxnZnrnIx7nFAHbUV5rrvxLuvD2nWus3KaRcafJIi3Fpa3e+5gVv4s/dYjjIAHPc9a0fEnjq+0bxN4d0yy0qG9ttb3mCdbjDNtUHG0qAo+ZecnjPHagDuaK4fTfF+tQ+P08K+IdPsYZLq1a6s57GZ3UgE5Rtyg54POB06c8ZHhLVPEuofFPxfb3NxZyQ2TWkRj+cLHEd7ARj+9yck9T7cAA9PorkLXxTfa3fa/b6KtkZdHnNsba4LeZcOFBzkH92pJKg4bO0niun0+4ku9NtbmWIxSSwpI8Z6oSASOfSgCxRWT4l8Q2PhXw/d6zqBb7PbLnanLOxOFUe5JArC1vxH4k8PeH/APhILzSrOa0iUSXdlBI3nQRnqQ5+VyueRheh5oA7OiuC8TfESXSbXw1f6Vp8WoafrdzDCkom2yDec4CEAZxnqwwetOfxjruk+ONI0XXtLsIrTWfMFpLaXDSNG6AHbJuUA9R0HfvQB3dFFebfE/Vdcstd8H2WnXEEVpe6rEsituzI6sCAxH8HQ4HJP0oA9JorhvFfi3XPCt34fSe2097XUrpbO4usvtt5GPBx3BGfT7pqTWvFmqaP8QdE0CS3sl0/Vw/k3bltysgyUK5wSflAOf4qAO1orCk1m7TxbNp+LUabbWIurmdiQ8RJYKp7c7WOewX3zWZpniTXPE2hvreg2dktm7P9jivGYSXSqSNxI4jyQcZDcYJxQB2FFc94N8W2njPQhqNtDJbyRyNBc20v34JV6qfXqDn0PbpW9KZBC5hRHlAO1XYqCfcgHH5GgB9FedeHfG3inxbpmqSaXoemwXVjfS2pN1dsY8oF+UbVyzZJ5IUAEdecJonjvxJ4s8If2xoeiWMUsAdblb64YBpE6pGFBJ4xyxGCcc8mgD0aiuLtPiAt58Lk8ZW+lXFy7RZ+wwZZzIH8sqCB03ZOcdOcU+78ValoniDw/p+rwWskWtM8Svbhka3lABCkMTvBzjPy/SgDsap6rqdto2mT6heeb9ngQvIYomkIA5JwoJ/GsiXxDcX3iO70HRY4GnsY0e9ubjJjhLglECjBZiBnqAB3ycVlf27quq+GfF1lrGjyWNzp8E8QmUN5N0pjYh4yR6Dkc4yOfQA6bQNbtPEmh2ur2HmfZblS0fmLtbAJHI/Co/EniGz8L6LJqt9HcSQRsqFbeLzHyxAHH415f4T8QeKND+DWm6ppmkWE2n6faPLN9quGWWZQ7FiiqCAAM8scnB46Z6zxN8QJ9I+HNv4w07S0u7eaCKUpLPsMQk2gZwp3YLYPSgDuVYMoYZwRnkYNLXFeOvGt94R8J2ut2+mwXSytGkgeYp5ZfoQu07h+Iqvq/jTX/D2v6MNW0eyi0fVrtbONo7gvPA7fdMgxt+oUnGDye4B3tFcvq3i2ODX5NCs7iwhvIYFnnmvptkcQYkIoHVmOCcZGBznkCqngrx0PE2p6vo13DbxalpcgDtbS+ZDOh6Oh649j0yPwAOzoory/wPqviXUviD4xS7ns5UtLmCBk+cCOMCQhY/zOSepoA9QorzeDx74o1bWPEuj6T4btGv8AR2iVRLefI+7cTlsDkgLtHHfJGK3Y9f1u98QLokOmJZzRWCXV1dXCNLCsjYHlJtKhsHPO4dOhoA6uiuM0Txtdaz4V1nUY9Nh/tDSLme1uLY3BWN3iGSVfaTggjGR1496wYviH4pvvh5H4wstB04WkUbzXEc1ywd0VyD5YAI4A6sQSQfl6ZAPUabI4ijeRgxVQWIVSx49AOSfYVyeqeO7Wy8KaHrcaIq6zNbwwGd9qQmUbt0h9FAOfUjGR1GvY3WrjWZrK/ht3thAssN3ArKHYkhlKknBHB6nOaAIPDvi7S/FFzqUGnfad2nSiGfz4GiIc542thuMHOQK3q8z+GXHjH4hn/qL/APxVWrjx5rMXgm58YnSY4LGCQ/8AEvuVdLhohJsLFuiseoXaR70AehVkeIvEdn4ZsYLu9juZI5rhLZRbxGRgz9CQO3H+Sa4f4l69q6R+D20i4iistT1S1DBgQ8mWDqCR0TgZxyfp12PHHjHWPBXhq31GTTLS8lkmWCVknZEjLE7SFIJYYHPI5oA7eiue8ReJ10fUdL0i1gW51bVZGS2hZ9qqqjc8jnBIVR6DJ6D1GbqXi++8L6/plj4ht7ZrDU5fIg1C13KIpuySIxOAezA9jwKAOzorhLnxrra/EW48JWuiWski2Ju4ZmuyFYbwoL/LlR1yAGOcdskWfCPi7UdV8Q634c1yytrbVdKMbM1rIzRSxuMqV3DI4x19aAOyoorzCHVfEtz8cdS01bizNva6XuhhfeEVGkQk8dXOBz07UAen0VzJ1zVtQ8Uano2nWSW0enRRs15eROyTu4yFQAjgDq2Tz24rLsfiRBP8NLzxdPYMj2XmRzWqPnMqttwGx0JI5xwD7UAd1WBpHjHStb8QaholmLoXmnorTie3aILu6cMAffpjBpLPUtXe70tnFheaffIzNc2m4CI7Ny4ySGU4PzcduOa4rSb+20z41+PLq6mWKNbWzGSCxLGNAAAOWJOAAOSeBQB6rRWDpV/rZ06fUNctra0iRXkWCEM0uwcgtzgNgfdGcetZej+KdV1zQdL1+xgsZ7K9uI0kt4yxkt42cKSWzhmXOWG0Y55OOQDsqKK53xL4nXRNR0XTVMKXOrXDQRTXBxFFtXcScYyTwoXIyW6igDbvbyLT7KW7nEpiiXcwiiaRseyqCT+Aqh4a8R2HivQ4dX0wyG0mZ1QyJtJ2sVJx9RUVtdao8mq2mpW8KrBGrQXEO4LMrK2Tg52kEYxk9jnmvLPhvq3ifTfg3a3WiaXYTW9ktxNI15OytMBI7FY1UHt3YjnjHegD26iuMPxFsV8A6b4n+yyM+pGOG2slcbpJ3JURhjx1Dc+gJx2pviPxVrXg6xh1fWLKzudK8xEuzZFxJa7jgN83Ei5IH8J5HFAHa0VxGoeM76y8f6Honk2Z0nWYjJbX2WJYqu4pjpk/Lg/7Qp1t4s1QfE+Xwle29lDD9k+2W9wpYtOmcbQM4BB3Z6/doA7WisTS9Uvr7xFrFo8dv9gsWSJJkzveRlDsp7fKGX67u2K2jnBxjPbNACOwRGc5IUZO0En8AOTWL4d8WaZ4ofUF037QfsE5t5vOhMRDjqNrYPHTkCuM+HuqeJNU8Y+Lvt9xZzJa6gttIMONiIGAWMdh356kk1l+BbjXk1zx7DoNlZyzHXp2aa+mZIl+Y4UBQWYn8AB37UAex0VxHhv4iW+peB9R8Q6vbfYX0uWWC+iRt4WSPGQvrncMD1OPeszXfiXdeHtOtdZuU0i40+SRFuLS1u99zArfxZ+6xHGQAOe560AelUVy994sQ+KdM8P2EttHPfWbXqXFwCUKAgBUUEFmOSeowFJ5p1x4ol0Tw5qmqeIbZIDYTGILASRODt2Fd2PvFgPY8E8E0AdNRXm+u/Eqbww+n3moto9zpl1OsE62F0ZJrXcCQx7OowckBf1r0jrQAUVx/wAQ/GF74K0SHUrbTobyJ50gcvMVZC3QhdvzdP7wrO8R+N/EPhXUNMudT0S0Oj6hdC0VLe4L3MbsCV3ZATJweASBj7x60Aeg0Vwl74w1/Q/FOi2Wt6Zp8enaxObaF7W4eSSGTHyh8qAc5A46c88c6134mln8Wt4Z0iKGS9gtxc3c85Pl26k4UbRyznrjIGOc9qAOlpk0qwQvK4cqgyQiF2/BQCT9AK5PTfGFxH40bwlrtrFb6g8H2izuLdiYrqMZzgHlWGG456HnpmTTPEmpeJnvbjQIrIadazvbpNdFibt04bbt+6meA3zZ549QDQ8MeKtN8XWV1d6WZzDbXLWrmaIxkuoUng84+YdcHrW3XkPwn1q30XwF4s1jUwbeG31q6mmQHcV+SP5R6nPA9TWprvxLuvD2nWus3KaRcafJIi3Fpa3e+5gVv4s/dYjjIAHPc9aAOq1rxlpWg6vpul3ou/tOozpb2+y2coWYgffIC8ZGcEn2roK8w+Js0dxrnw7nhYPFJrkLow7glSDXVah4mZfGlt4WtHtoLuWyN6ZrkFgV37QiKCNzcMTyMAdD2AOlorN0S71C7tJTqdrHbXMU7xFY2JVlB+VwSAcEYPtnFXbjz/s0v2by/P2ny/Nzt3ds45xQBLRXmHwm13WL/wAL6hrOtXlq9t9suJLiZtwdSuM47BABwOwFb2n+I/EHiDw9/b+iafZi1kDPaWt07CW4QEgEsOIy2OBhuoyR2AOxorB8H+K7Lxn4ei1ayR4gWaKaCT70Mi9VP5g/Qit6gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKpajPpkcWzUpLURnnbcFcH8DWR4q1e9tTbaZpSFr+7ztI6oo6n/6/bBrCg+HV1ckzajqn75uWCqXJPuxI/lQBoyP4FL/MLLP+yrAfpVywbwgJB9kOmiT+Hdt3fhu5rNHw2scc31yT7Bajl+GluR+51KVT/txBv5EUAd0CCAR07UV55BHrXgi5ie4l+06S7hX2kkJnvg/dP6GvQwQQCDkHvQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAeJeDPEeh+BptU8K+MoUs7q3vJZbW5nti63MLtkEMFOec/njqDW38R9VtZ/g7rs62y6fbXOxbOOVPKknG9Pm2HBGecAjOACcZwPUGRHKllVipyCRnBp1AHinxQ1Swm+A2lrFewO0yWgjVZASxUAtge3f0rW+JuoWkPiDwBrzXEZ0qHUWMl2rbo1DBcEsOMcHn2Neq010SRdrqrL6MMigDyP4qa/pNxd+BZo7+DyxrkFwSzbcRK3L4PO3uG6EcjipPHetaWvxb8AOdRtdkLXLSt5y4QOihCTngHt6161RQB5j8VjPp2oeFfF0Vm1/p+kXEv2yONd/7mVVUvjoQADz6kVs6P4w8MeIbq3i8LwRX85ZWkkS0aNLZO7OxUYOOAo5Jx2yR2tNREjXaiqo64UYoA8p0vVtOf9ovVdt/bEPo6QIRKuGk3odg55b261d+KdndaPeaN480uAy3mkTCG5iXgz20h2lfqC3HpuJ7V6XXDWsHjDxDrP2bxFpdjp+i2d2LhDBP5jXmw5iBGeFDBWOcElQMdaANGG5t/AngM3+ss5MCG4vniTcWmkfc5A7/ADuceg+ldBYXsOpadbX1sWMFzEs0ZZSpKsARkHpwanZQylWAIIwQe9LQAVyHxQ1Czsfhxrq3VzDC09lLFEsjgGRyuAFB6nkdK6+igDy74XeM/DGn/DTRbW98Q6XbXMMLLJDNdojqd7dVJzW1ovj3w9d3eqrplxbJoWlx+ddXxJWMzSuzEKT16MTjqWAFdvRQB4l4hub3SZ7H4vWkkYjmdYbixBUebYtgJz/FLxuI5xkDohzp/F/WdP1r4X2Op6bdJc2kmo27q8R3epwQOQfbrXrVef8Axc0bWvEPhq203RNKlvZxeRTswmijVVXOQS7A56dBQBW+I+oWPjHwg3hrQbu3v9R1SaFESBw/kosis0kgH3FUKQc45OOtY2r3Vp8P/im2oa7as/h7VNPhtkvGhMiwSxDaA3BxkA/99Z7GvWrRjLD5zWb2sknLxybN4+pQkH8zU7KrqVdQynggjINAHLaN4n0LUWlvtJjgTSIYyZ9TeLyIi2RhFLAbu5J6Dgck8cr8Bb+0m8BPbR3MTXEd5MzxBxvVSQQSOuORzXqgAAAAwB2FFAHk3hvWNNl+P3isx39syy2VukbCVSHZVTcFOeSPanfD/VdPk+K/xCKX1swmltmjIlX5wiPvI55A7+ler0UAeT/C7VtOfxf8QNt9bEPqjTKfNXDRjflhzyvHXpXLaNajxT8O/iTpGlTxXF9PrE11BDG4LSoro4KjuG2ED1NfQNFAHmHh34k+EdW0q2iexzr2wRy6WliTMZhwQPlxjPckADrjmqfjvUra3+JXw3W9uLW3nge4NzGso2wl0jABPYEggE4zivWQiBy4VQx4LY5NOoA8o17VLBf2hfDSte24MenTRvmQfKzB9qn0J7D3HrR4b1Sz0P40eN7TUZvIn1A2j2kZUkzgIQdgA+b7w6eh9DXq9N2IXD7V3gYDY5xQB5D4o0zw/rk2reJNJ1keH/FWlSzRSSRThTMYyQodD94MFHI9cHdjFekeFbvUr/wppd3rEPk6jNbI9xHt24YjuOx747dK1mjjZlZkUspypI5H0p1AHDfFzw7feJvh7e2emo0l5E6XEcS9ZNhyVHqcZwPUCoNV8Z6Xr/w1vfs8qTanf2ElsNMQ5uPtDoVMZj+9kMeeOgz0r0Cm+WgkMmxd5GC2OcfWgDw7xbZw+EvCvw10TULyBLqy1W2lnUyD5FBJdv8AdBJGenFbfxF1bTl+JXw8LX1sBHczSOfNXCq6x7WJzwD2PevWKKAAHIyOleYfF2VLDUfBOq3J8uxs9bja4mI+WNTg5Ptwfyr0+muiyKVdQynqGGRQByni/SrXx98Pr+1sZBN58Rks5QCAZEOUKk9iRjI4IPFeeXWnaz4z+G//AAml1C8OuafFDPpqkfMv2fmR8esjbzj0VK9wpNoC7QBjpigDj9D06513wVqV3dqba+8RQySspPMKPHsiXP8Asptz7lvWsL4Ya/aaB4MXw/4iuIdM1TR2kimhunCFk3Fldc/eUg4BGenuK9OAwMDpTWjR2VmRWKnKkjOD7UAcH8LNEutO0/W9Tu4JLY6zqk17DbyKVaOJj8m5T0J5OPTFd3LLHBE0ssixxoMs7nAA9SafRQB5R8GdW05dK8VM1/bKo1y5uCWlUYiITD9fun16VB8GtX02L4Z6oZb62jEN3cPJvlC7FOME56A56169RQB4H4Y8WXfhz9nOO70Ro5L+2neOUqA5tVeZvnZfoRjPHIqz4o1jw1/bvgXV9PvJLqzh1H/StVmDtkkDAaRh16naOF9BXuRAIIIBB6g03yo9ip5a7VwVGOB9KAPIYfEtv8O/ibr8uvrPDoviDyrqzv8AymZFZVwUYAZB56YyMDPBrr9V8U2OreCNe1CEmLSfsMqw3lwDELhijfcVgCV6AHuScdMnsHRZF2uoZfQjIp1AHjmhatp6fszzhr23DLpVzblTIMiVhIFTH949h3rL8R6rp7/swWdul9btM9taxLGJAWLrIhZcdcgAkjtXu9FAHjvxf1bTp/hBp7Q31vIJprYxbJAd4Xk4x1x3qz8YNY01oPBrLf2zKdct7kMsqkeUNwL9fujPXpXrNFAHj2p6vY+C/i3qeqa/AH0DxBbW7QX/AJXmxxyRptCkgHqMnj1B6Zx33hzW9M1y4efQrRTpqphr4QGJZXyMKmQCwAzk9M4AzzjonRZFKuoZT1BGQaUDAwOlABXk/g7VbLRPit46sNRnFvdXt3byW0TKd0y7W5UAcgBgT6DnoDXrFN2Jv37V34xuxzj0oA8p+Hmr6dN8WPiAsd/bObme2MG2VT5u1XDbeecEjOKsah4wgPxU1Hw/4jupLLS7a2jaxt1DAXzsAWLbRl+pUJ0ODkEivUKaUQurlVLL0YjkUAeLeANX0zT9B+IVhNIllKmo3s/2eVfL8uIqFXIPC8jAFJ4e1Swj/ZhuEe9gV1sLqBlMgyJGeTauPU5GB3r2sIqliFALckgdaWgDyzS77w1efCHwnp2u/ZrjTL9IbGZ2kwsEghZgSw+6QUAzxjNL4Htr7wv40vfD9nrb6r4VisPtKyTuHNi+7AjLjjlQTjjjnA7+pEA9aakaRLtjRUX0UYFAHknw41m1/wCEm+IsllNDdTtqDz28McgJnAD42+oJGMiubvvE9l4t+D2sXWoXMt/4mZHaa1ETFbILJxtTG2NQg+8eTyCSeK+gaaI4xuwijfy2B1+tAHi3jHVLO58DfD3WoJvM06x1Sya5nVTtjCr8xPHQEEfXjrV341a/pV98ObZ7a+hkWa/heLDf6xQTllB+8vH3hxXrhjQx+WUUpjG0jjH0p3QYFAHlvjrfZ+NfCPju0P2vR7MyW95LbnzFijkBXzflzlRubJHoKm+IK2vjpNA0PRLmG9kOpRXk81tIHS3gRWy7MvAJ3YUd+1emU1I0jBCIqgnJ2jGTQB5XHqdh/wANJSr9sgz/AGH9mx5g/wBb5oOz/ex260eFdTsJ/j54xEV5A5ltbZI9sgO9lRAwX1I746Yr1aigAryiXU7Pw/8AtBX8+qTC2ivdFRLZmBPmuHX5Vx1b5TwOTivV6aURmViqll+6SOR9KAPLYvGNrffEfXtG8Tzy29vZmNNN03y3xdAglnZVGZSflwpyMHoSM1j/AA413R9N+GuuWmpQLIlvqUiX1nJGf3cUkqRncMY4DHjvtPpXtWxC4fau8DAbHOKAirnaoGTk4HU+tAHkWjaQfB/jvR7XwbrTX2gao0jXWmmYTJbIFz5qsCdozgZPU4GTnjA1DTLLxd8XPF66drMNvqkMdpcaVdRzjHnxxqCODhh1B64/DFe+LGiFiiKpY5YgYyfenUAef+D/AImWXiHw7dG6aCHX7COQXdgZAC7xgklOuVOD0zj9TyF5ptjok+neKvhvq3lS6jcxLNoaSB4rjefmHl9UKgnP90A424r2+miNBIZAihyMFsckfWgB1cd47i8NaqdP8PeJ44xbagJWguHk2GKVNuNrdiQ59jjHOcV2NIVDAhgCCMEGgDy7wZPqHh+fxNpN9rv9p+HNNhQ2uo3LjMZYHMRfoxAx3446ZxWd8M9W0+D4AziW9t0a3gu1lDSAFCzOVBHYkEY9a9iRFjUKihVHQAYApaAPn22sLvVvgT4U1DRF+23mg3wu5bWFtzkLI+RtHO7kHHpmu88da9pvi34eXGnaDcxajfasiRW1rCwaQEspJdeqBQCWJxjHNejU0Rors6ooZvvEDk/WgDzjx94WuIfhhp5sG36p4ZjgurWUcljCoDe/KgnHcgVg+IrLUotI0v4nrZSf2zDepdSWoOWFlIBGIPrt2k8cF3r2cgEEEZB7UhAIwQCPSgDK8NabNpmhQRXRDXspa4u2HRppCXfHsCSB7AVrUUUAeTeA9XsdG+IPjrTdRuBb3lzqgmhicHdIjbjuA9ACCT0A5PFL8KNX05tc8ekX9thtamuFPmrgxZPzjn7vv0r1bYm/ftXfjG7HOPSnUAfP2gWv/CVfCr4haTpU0c97NrE11DDG4LSIGicEDuG2ED1Ndl4d+JPhHVtKtonsc69sEculpYkzGYcED5cYz3JAA645r0+mhEDlwqhjwWxyaAPPfFuneG/FF9a+HvEvlafqcVlHd2l1DMI2icsyusbHg4KA4PUc4GK5K7svFeufCrXrB7ltZk0nUlbT7wLlr2KJgT678c885IIycV7e8aSKVdFZT1DDINOAAAAGAO1AHm+j/Ebwdr1lCunWAuNZlUD+y0sz5iydwzbdqqD1YnAH5V6NHvESeZt37Ru29M98e1ARFZmVVDN94gcmnUAeW/Hm8tofAsEEtxEkr38LLGzAMQC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z/exzjrQB0fg7xZqGsazrmg61ZW9tqukSRiQ2shaKVJF3Ky7hkcDv6is6Xx7ql54W1XxTothaXGmafLKogldlluI4j88gYcJ0JAIPA6jpWb4N1KxuPjf46EN5BIZo7QRBZAd5SIB9vrg9cdK53Stf8I2Fr4h0FfFY0jS7+6nSTTrmycz24b5H2SfdXcASAQ20Ed80AdT4v1q48Q/AvVtWuLKO0W7s0lhjSfzcxtsIJO1cHkjGO3WtqLxLb+H/AAj4XhYwm7v7aGG3WaURoCIgzM7HooA+pJA71jeP7/QYfgfex6VfWrae1mlvZFJQQ4VlUKvPJGOe/BzWH4ku1tdD+HnjC3hGpaXpEXlX6QAS7ElhRWYjp8uD16HFAHWaZ4+f/hO4fC2onT53vIDPZ3mnylkYjJaN1OdrAKT15/Gu7rjtD8WeG/EN7AnheCK9YHdPcJatGlumP4mKj5j0CjnnJ4Bp9t8PrS21OG+XXvEbtFMJhFJqkjRsQc7SvQr2x6UAU38Z65J4/v8Awpa6LaNLBZi5ime7OwgsAGc7MqOfugMc45xyK+ieM/E2r6jrPh8aPpya5pUqrNMbhxa7GGUYcFyTzxjtyR0OdpupWL/tFaqq3luSdFSEASDmQSISg/2sdutN8E6tp0nxi8fbL62YTfZDERKuHCRkPjnnHf0oA6Lwz47XUPDutX2uQRWNxodzNbX6xMXTMfJZM8kHsOvFNl8T+Ij4SHim30q0ezMH2sWDSMLgwY3bt4+Xft524PpmuD8P20XirSfilo2nXkD3d7qU8luqyj94M5Uj/ZJGM9Oa6vw34w05PhlDa38gi1aysvsU+mycXDTImwII/vEtgYwO/wBaANibx9Yz6Fol9pMf2u511xFYW7ts+bneXPOFTB3Yz0wOtcp4zbVl+JHw+i1JbR1+2ysk1sGUE7VypVicY4wc8+gxzzz6FqXw90X4c6zfwSyW2jSXH9pLGC/2YXBznA/ugkH3A9a2PGfivRtY8b+ArvS7v7dbQXsrSS2sbSKuVTj5Qcn1A5HfGRQB2l94w83V9Q0zS7rSoJdPKpNLqM+1WkK7tiqOTgEZbPBOMHnGTaeM4vG3wr8S3f2cW11a2l1bXUAfequsR5Vu6kHg/Wufstd0rwL8QPEth4sgWCz1S6/tCwvprcuj7gNyZwSMcD8DntnqtZ1zTrj4eeI76GGOx0yWxmjtZpk8g3LGNuVUgHBOAuRk4PGMEgFz4Xf8kw8O/wDXmv8AWrniPxKNG1TRNKQwx3OrzvFFNcE+XHsXccgEbieFC5GSevrnfCi6t7n4ZaEsE8crRWypIEYEowJyD6Gl8d2/hrWH0/w74mREgv1lkt7lpPLMUsezG1uxIc9eDjGDnFAEusat4itPDHiWYRWkF7p0TyW1wFYxyoI9+/aeh+8MZIyOpFZHg7XdV034S2+uahD/AGh5diJ447fc08zEnO7OeSSOR7msvw/Bri+GvHXh2TUZNb06ztXg0y+b5nkLwsWi3DO4qSo9icewpaN43Ol/AJLnw+y3Wq6bZIkiBC32c7wpLjGMgEtg9QM9M0AdjN4t1PSdS8Nxava23k64/khYQyvaylQyq24neOxOFx6Vj3uqeJH+OKaVBcWf2WLR3uIIZN4QBpFUs2Or/Lx2A/EnlfEOt+HpZ/Ams2l7LdxxanE17qk6s2CVztZyMA5ydi8LjoOK6HVdVstH+PVhqWoTfZ7O88P+TBLIpAeTzS2wcZ3Yxx15A70AdPfeMPN1fUNM0u60qCXTyqTS6jPtVpCu7Yqjk4BGWzwTjB5xJ4C8aReNtDlu/s4tru1na2uoA+8K645Vu6kHg/WuFsde0rwP4/8AElj4sgEFlqt1/aFhfTW5dH3AbkJwSCOBjtg57Z7qCCz8Y6NcJaLe6bpkjoYLm0ZrSafHVhwCEPAGQCcHtgkA6DUtRttI0y61G9kEdtbRNLK/oqjJrlJfE/iI+Eh4pt9KtHszB9rFg0jC4MGN27ePl37eduD6Zqvq/wAPMeC9f0zT9U1a8ub+12RjUL5pgGXlQN33cng1W8N+MNOT4ZQ2t/IItWsrL7FPpsnFw0yJsCCP7xLYGMDv9aAMj4r6+3iD4LSavpMkR0u9SFn35Eo/er8vHHBGD9DXqGmf2l9nP9pfZN/Gz7NuxjHfd3rxPxZpZ8Jfs222g6lPHHqTmN/IZxu3NP5hUDvtBwcehr1u/sLDxhodusGrXcdsWWVbjTLwxs2FIxuXqOen0oAk8XXl/p3hHVr3THhS8t7SSaN5VJVdqkk47nA47Zrz63uNUu/2eb641GWGVZdHd0kBYyMSGJLk9T9K6q68KLo3grxHaWd9quoS3djMqi+u2uGDeWwAXPTOf5Vw1l4k0ef9nO4s0v4vtEGkvbyoTjZJyAhPTcew6kcjigC/b+L/ABH4U+GPh/W/7GspNCtbCzjnD3DfaWQoieYoA2gZIwCScEZxyB2+teLLew1Gx0u3mtBeXkLXCyXUvlxRRLgbmPUkkgBR155GK868Warp8n7MlpGl7btI+nWMCqJAS0iGLcoHqMHI7YqXXtVtPD/jLw14xvIlvPDd3o402e5jTzkgYPvDHGe5A9eG7jFAHXeFfHZ1jxVqfhm+W0N/ZRrPHcWUu+G4iOORnlSCwBGT+ldtXJ6PrWieKHlj8PQ77JoXSfUYITCFJGAkbEAl++RwuOeSKdo/gW10bVIb+PW/EFy8W7EV3qTyxNlSPmU8Hrke4FAFK58Xay2l+JNWtdNjgtNElmQQ3iOr3giXc7q3AVTyFOGzjtUHib4izaP4N0fxHYaULm21MwACSba0XmDI+UA7u44I5rjT4rt/FHhzxpBrrTSa7ELuC00jy2YQIsZCMEAwSDkmRuRjjAxnO8Q6/pU/wM8Iwx38JlhubNJELYIKA7+D1x3IoA6/x54g8cWHgnxLdyWFlpcMKxC2uIrjzZGjdtrDj7rjK89Bk4zgGu78M/bzoFmb/wCzl/JTZ5G7G3aMZz3rm/iuV1T4Ra49g63KNCkitCwcMqyKzEEdQAD+Vb3hHW9O1zw7ZTabcrcRpbxqzoDtDbRlc9MjuOo70Acd8fP+SW3P/XzD/wChVR8c+N9Ml+HUmjCw1D7Tf2q20TXtlJawIxAw7SShVAHUHPUCpfj/AKhZxfDuSxe6hW7luImjgLjewBOSF644PNXtf8c+E7j4eXFlHqNpqlxcaf5MdjasJpHdkwoKrkrzjk4xigDQi0uTRfgjNps1zHcyW+hyqZY23I37pj8p7qM4B9BUXwU/5JFoX0n/APR8lYegW114T/Z9urXxHOtpO9jdCKK4cKyb1YpHg/xHP3evOKPhF4y8M6Z8L9Is7/X9NtbqHzhJDPcojrmZyOCc9CD+NAHrEv8Aqn/3TXifwO8SWuk/D57eay1WZ/tsrbrTTZ506LxuRCM+2a7zTviHous3OryWd7B/Y+mRKJr+RtiPK2flUnGQAvJ7lhj34T4F+KvD2kfD97TU9c06yuBeyP5VzcpG20hcHDEZFAHpXhLWbfxLDe6zFZPbuZ2s/wB7GUlKRMdodT0OXc47Zrivgd/qPFv/AGGpf5V1Og+MdF1rxXcaT4deC6gjia6vrqEkoJCVVVU9CSNxJHHy+/HH+CNQs/h94u8UaD4hnTTkvb9r2wubk7IZ427BzxkDbkZ659KAO48XAG/8K5Gca0h/8gTV09cddanY+K/EWi2+jXMd7b6ddte3d1bnfEmInRU3j5SxaQHAJICnOOK7GgDkYvFF/q+o6/aaKlk02jSeSba4LeZcPsDZyD8iknaDhskHpVu+8TSQNo9hbWqyaxqkfmJBIxVYUChndzjIAyBjGSSBxyRwni3TtB1u41fxDpesf2D4s0h5YnlimCmbZ90Op+8GAAyPp82MVBrOr6v4e1rwb4812xm+zvpn2PVRDGSbZn+YMV7ckcexHXGQDvtO13WY/FV1omsaYoto7UXUOqW6ssDDIBRt2drDk/eOQM8VV07xNrXiXRJdb8P2dkbLfILSO7Zg92qEqWyOI8kHGQ3qcZqay8Tad44srq00GSW4spbeSOW+8p40RmXAVdwBZucnHQDnkgHm/hhrlr4b8HHw54juIdN1PRpJI5YrlwhkjLlldM/eU7sAjPT3FAFq++KY/wCFZy+MNL0vzxA/k3VrPN5bW8m5UwcA7sFge3B7VePjDWdOtrzWdd0eC08PRWK3EM0c+6ZpCVAjZD0LZ47DgE+nnGtac2hfAnxRJqOLO41zUze21pMdsgRpo8LtPO7au4jsDz0ruvG1pJ4p+Dcq6FIl5MsEE8SwsH8wxsjlRjqcA8euBQA3WPiRN4cuNMudROjz6beXC28y2N35k1oWBIZuzqMHJAH416NXm+i/ETwlr9jBHp2nrPrkqgHTFsyHSTuGbbtVQerE4x78V6Ou7aN2N2OcdM0Acva+JL7xBqeq2ugR2gt9Lm+yy3V1uZZJwAWRVUjAXIBYnqeAetQ+HfF914kg1WwjtILHX9KnEF1bTuXjHPDqwAJVgDjgf1PN+BLhPBXiTxR4e12dLNbnUH1GwuLh9iXMb9drHgsoC5Gc9fSrvhSGKLxj4x8a3EqWukXrQw200zbFkSJArS5PG0kfKe/NAFP4HSapN4EtJJGtWs3nuGYnd5pYuc+3WvTLu7gsLOa7upVit4UMkjt0VQMk15r8Br61m+G9vax3ETXEU8xkiDjeoLkgkdRwRXbeJ9AbxJpS2K6jcWBWdJhLAFJyhyAQwIIzg/UCgDzvx1azz3Pg7W9QiaO8uvE1ksUL9baAFyqf7x+83ucchRXr1eJ/EPwpq1ifCv2jxhqt75/iC1gj85Ix5LNuxIuFHzDHGeOa9as0XQdEjTU9Wa4EAPmXt4yoW5JyxGAOuPwoA4f4HDHgS4A6f2ncfzFelV5Z8Cb+zn8F3MEVzE0w1CdzEHG8KSCDjrjmu6tfFGm3niq/8OQtL/aFjCk0wMZCbWwRhuhPI/P2OADy5/FV18I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4TUUScRxLPAwl35xhhJgdec4/GotH0DxDZfAmfwxNoU41f7LParCLiAhjIzkNu8zGAGGc8+xoAPGXifXP+FI2virT9SexvntbaWXyo0IcyFFbG4EryxIxVj4n6xrWj+DtF1DStVmtZpbq3glUKpEgcEnJI3A5A6Ed6oa74d8SX/wLtPCsGgXB1UQW8Dxm4gCp5bIxbd5mCDt4xz64q78QtH8QeIfBGi2GnaBcveQ3UE80T3EC+WI1IIyZMEkkYxmgBviafW/CPjDwrdr4hvr2HVtRWxvLWcIIcPgAxqANmM+pPAyTznW8Sa9cn4j+H/ConltLK+gmuJpYm2PMVB2xq3VehJxz05Hen8QtL13XNT8I3Gm6Fczx6dqUV9c5ngUoqsMrzIMt16ce9R/EGW9vdf0e0tvDS65HbRNeSQQ3Ygurd8gI28cqOv3SCxHXCnIAeHpfEMfxT1nQJtevJ9IsYYrqFZI4nOH/AOWbuV3dc4Oc4FejyJ5kTpuZdykblOCPce9cF4V8QvBqq2F94M1PRJb+Q4u55BcCaQKSA8uS2dqnGc9MV3zEqhIUsQMhRjJ9uaAPI/AukeZ8UfHsf9o6gv2eezO5Z8NJ8j/fOOeldFpGsXHjLxj4jsftk9rpeiyLaLFbSeW80pzvdnHzAArgAEd85qp4N0zXdO+Ifi3U7/Qrm3sdXlga3lM8DbRGGU7gshIzuzwDUtn4e1Xwh8QdY1nT7KTUdG1sLJcwwOgmt51z82GIDKdzdDnnpxyAR6Prer2HxD1PwPqGoyXEcloL3Tb51UzIhOCjcYbBzgkfw85zxjeA18V+NdAnvLrxfd20tpq8iBoYU/fKm3KsMDjHAAwBkk7sjHU6foV7N42vvGl/YypKLJbGxsFdDKIw25mY7tgYk8DdwOpyeKXwm0fW/D+iX9hrWkTWUkt/LdI5mikUq+3A+Ryc8Htj3oA9Crn/ABc3ij+xpk8LQ2DXrIQJLqZlKH/ZUKQT6ZIAPrXQVy1l4l12S+u7S+8HX8JikYQTQ3EMkUyZ4bcWXbkc4wf6UAZnwk1e21DwYLGLS/7LuNKnezurTcTtlHLNk8nJJJzznPWus1zSINf0S70m6kmjt7uMxSNC+1tp6gH36fQ1k+HNAufD+navdlIp9W1K5lvpo0fEfmMPljViOgAA3EcnJ9qr/wBt+MI/BNvqT+FopdcL/vtNS8VcJuIyG5GcAHGe/wCFAHA65oPiH4RRR+IdD16+1Pw/BIi3mmXz7ysbELlT06kDIAI46jNe1I6yIrocqwBB9RXF6xb6l480FNHn0e70mzumjN9JdvHuCKwYpGEZiSSAMnAAJPJ4rtgABgDAFAHGa9rl1eePdM8HWNw9qJbV7++uIseZ5QO1UQ/wlm6nqB0wTmuY1fT30/49eC0F7c3EDW12Y0uH8xoz5T5w5+Yg8dScc/Stbxh4d1208eaV428O2iahLb25s72wMqxtLESSCrNxkbj37L15qnf6f4r1n4l+GfEkfhwWtpp8MySR3V5GHG9SuTs3f3sgDPTnGaAN26XUP+Ei1U69qzW2nOI00e3sJ2WZjtPmOUQbnbOMDkYHT1x/Cmo6945+DwuU1KW310CVIruLCb5EYhdw6YIwDx70aLZeLdG+IPiW4n0NdRi1KZWs9Ra6REhhGdsbA5YAZHCjqCferfwj0fXPDvheTRdZ0w2rW1xIUm85WEwZs5UKSQPrjt+ABR8O+LJtU+Dsl359y2uJmwkDykSC9LBEHtlmQ4HABPHWrnjTWrrwZpvhwXlzfS6P5/latfx5M2Nh2kkcqGc5JXBwMDqBVTT/AAmbf4zalNaz/wDEpaKLVLi2A+VbxvMjX8xvf649BXY65LqUeo2KQaU2o6VNHNHfRKY8rnZsO1yNw++CB2J9hQBH4YeO5N1fWGs/2npFyI2tCZvN8ogEOu7qex+Yk8kdqPG/iQeEfBupa55Yle2jHlxt0Z2YKufbLDPtmsLwR4PPh7xTr+oWVpJpmj3oiEFgzA/OAd8gUEhRzgDOevAGK6Lxf4ci8WeE9R0OaTyxdR4WTGdjghlP0DAUAc/eaZrreBxq9r4gvRryWn2sPuBgkfbvMflY27D90HGehya5bxR471fU/g1pfi3Sb+XTrqaZIbiKFVKsd5RhkgsOVyMEcHvXUaZJ4rtvBK6BcaHI2rw232OO8E8ZtnwNiylt28DGCRt3egrB8XeAdTtfhJpng/w9YSajPBLG7yCWONchi7sd7DqxOAM0AWvGE+u+ENd8NaqniK9u01DVIrG8s5FQQFX/AOeaAZXGDjJJ6ZJ5z6hXnPxI0zXfEMXhk6ZoNzKbPU4b+4Vp4FMapnK8yYLc9sj3r0SNi8asyMhIBKtjK+xxkflQB5/P4juNc8V63pUcer/YNLMcH/Es2qzysu5izkggDIAA9CTnjEng2Xxhcafrmna215beRMf7M1KeOMyyREtjcvKlhgZyP4vas+90nxX4Q+IOp694f0lda0nWQjXVotwsMkMqjAYFuCOSe/U5xgGussbjxDLZ3Oo6lpnlStHtt9Ktp0kZfUvISqliccA4AHU5NAHO/DPxLqeo/Chte1OaS/vU+0ysSAC+wsQoAGB0xwK5+51nXL74MDxxZeILqHWFU3D7GUwYEpUxeUQVAA6HGSRyTmt74Y6fq/g/4bvYa3p4sLmy86XfcXERiYElgSyM2B65Fczocmq2VhG918Lr6f7RILqYW9+Ps8shO7eLfIRexAIyMDJyM0AXPiiL4/CnR5L27kububULWVmnjRSpYE7SEAHB46Ve8YSa74N1bw5rCeI7+8W+1WKyvbSYIIGWTP3EA+TGDjknpknnK+Phqvj3wNp39gaHeST/AG9JZoZ3ihaAxMyujB3HzZ44yO+aufEzTNd8RWXh5NK0K5na01OG/nDTwJsVNwK8yctz2yPegDM8Vaebj49eFk+3XsYmsrg/u5tvl4Rvuemcc+terRJ5USR7mfaoXc5yTjuT61574s0rWx8Q/C3irTdHlv4bSGaC5tkljSSPepAPzMFPLHOD2r0CBpXt42njWKUqC6K+4Ke4zgZ+uKAPHfD7G/8A2nPEL3nL2en7bZW/gGIhx+Dsf+BGvXr2S6itXeyt4p5wMrHJKYw3/AgrY/KuF8W+B9SbxfZeNfC0kK6zbL5dxazsVjvI8Y2lh0bHHPHA6Yraj8U6u9uVPgvWFvQP9UZbfy93/XTzMY98Z9qAM3wH4w/4Tbw7qOqQaLHptr5kiH9/veSQKCSQEA6Ec5zx0riPglq+rWPw6ki07w1d6kftkrLKtzBFFnC8Es+4fgp611nw+8Laz4F+HVzp91ai+1CWaSYW9pIvG5QANzlR255796i+DWh674V8LS6NrmjzWkouXmWXzoZEZSFGPlcnOQe2PegCTwX4K1Dw7a+J9b1qWBtX1tpJ5orckxwj52CgnqcsfyH1qn+z/wD8kwj/AOvyb+leokBgQRkHgg15f4P0vxB8NPt+i/2Hc6xocly09ndWMkZljDYGx0dl6YByPf8AAA6nxJ/yNng3/sIT/wDpJPWH4q8WxaJ8QLTTvEFzc6foFxZZtrmJ2jje53nId1wRhcYGcc5PbG5awanr3iGx1W/019NstOWRraCeRGmklddpdghKqApYAZJO45xjlNXiuL7Ur/TdW8PNqmgTQRmJh5T4k+bcCrMG/ukMOhz0oA1fD0NzBo6Jc6j/AGiTJI0V0WDGSJnYx5IABIQqMjritSvOvDOh+IvBHw91ODSrEXd8byWfTtNuLgfuYWYBY2fOMhQWODjJIzXXeF9WuNd8M2Gp3dp9luLiLdJCG3BTkjg9wcZB9CKAKnixdZdNPGnXtvZWAuQ2p3EkgR1twMkIxGAT0zwR2I61zug+IZR8V7vw9a3tzdaPJpQvYvtBdikgkCHY78shBznJGehqT4j6Z4gu9V8M6hpOnnVLDT7tprzTxKqGU8bG+YgHb8xHoSPwpx6d4pHxetPEsugKLK50r7E4S7Qm2/ebsue59lB69Tg0AemV5PoPid/EQ1HSbzWrrSfGkN1IFtZZDHGVDkokaH5WUpgE4LdWz0NesV5X4m8M6h408IQW+p+GpIfFMSokOoCSICNgw/eeYrZ24ySuM5PA6GgC74+1PXNO8ceD7TTNYlt7fU7mSKaBokZBtC4PQMfvHgtjOKpanJ4g8PfE7Q9GtPEt9cWuuwzCU3qpKYGjG4vGAqqpxwBjAJyQelXfGGia9e+K/BVzZ6Xc38GjTNJd3ImhTfkIMgM4JPyknj86d4o0rXLz4oeFdas9DuZ7DS1mFxKJ4FP7xNvyhpATjv8ApmgCOyutT8OfF218PSa1falp2p6c9xtvmV2ilVjypVRgEDp05+laN6NRXVdabXdXe2gfA0a30+ZhLtC/M5RBuds44O4DHTHWnq2la5P8ZNG16DRLiTS7OyktpZxNCPmbdghS+4gZHbPtVTQNP8X6T4v8Ui50Rb0apcl7bVXukVI4eQiMOXwoPCgdc/WgB2l+IL3xR+z1qGq6i4e8l0m9SVwoG4osi5wO5Cgn3qG3/wCTaT/2An/9ANV/C+h+J9M+DGpeFrnw7ML77PdWsIS5hPmmXfhuWACjcOpz6CtCLR9fT4IHw2dCuf7V/s9rLyfPgxuII3bvMxt/HPtQB0/w9/5Jz4b/AOwbb/8AosVk/F+08/4a6zN9onj8m3J2RvhXyy8MO9bPga1vrDwVpGn6jYyWd1Z2sdvJG7o+SqgEgoxGOKp/Emw1LV/AupaVpOnS3t3eR+WgSSNAvzA5Yuy9s9M0AYxhv9G+Djappus3sV3Fo0dyjSlZVUrDu2qrAgA9PwFS+B7DxBq2meG/El74nunVrINPYeWvlTbk+Uk9d2TuJ59BgVNd2esz/CCTRF0O6/tR9KFh9n86Dh/K2Ft3mbduffPtV/wdY6vZ/Dqy0m5s5NO1O1shbqZXjdRIFIDAozZGcHmgDkPEOqa5oPgPUNS1PXGHi22bz/JspGkhhTzBtVowNoQpxlxknvmuj1LxTeXuo+FdDsZfslzrcBuri4UAtDCsYYhAcjcxOASDjBOK4yz8PeM5vhJqnhKbw2ItQYSNLey3kbfa3Mm/IwSWc8DLEDgc9q09W8M+K7rS/CfiTSdPjtfEOhReS2n3E6kTxFArAsDtBODxno3UHigDrodE8Qaf4wgns9Xebw/Jbst1bXkpklSUZ2vGxBPPGQTjg8dMcd8MNLP/AAl/jdv7QvybbVig3TlvM+8AXz96uz0DUfFWtTxTatoaaDaxZLxNdLcSztjAHyjCqOuepIHQZzgeFtL8QeG/HXikNor3Gn6rei7hvVuI1RVOSwIJ3bucAY5I5IHNAEngbV9XufH/AI203U9UlvbfTntlt/MREEasHJ4UAenPtTvBuoXXxC0e812fUby0tpbqSKxgs5fK8mNDgMxH3nPU7sr0wKj8GaTrlh8RPF2p6holxbWOryQtbytNC2BGGHzBXJGcjHBpPBuh6v8ADx9T0dNNuNS0WW5a5sJ7V498QbrE6uy9MDBGR1zigClpHirXbrQ/Gmi3uotHrXhwOY7+KJMzptZo2ZWBXPy84Hcd+ayvP8S3fwXg8XnxXqEWo21qbiNIggifa5z5gIJckZ7gDjjrnpLfwpqdlonjHU5LMz634iWTFpDImIl2MkSFmIBI3fMc49M45oQaB4hj+BDeGDoU/wDa/wBla18j7RBjJYndu8zGMH1z7UAeg+HtRfV/DOlanKoWS8s4bhlXoC6BiB+dc74y8R3dp4j8OeGNPn+zXOszP5tyAC0MMa7m2g8bm6AkHGDx0rY8G295ZeDNGsb+0ktLq0sobeWJ3RvmRApIKMQQSOOaw/H3hTUNXvtD8QaJ5batolwZUgkfYtxGcb493YnGATxyaAM/xvqmo/DyXS9dtdQurrSJLpbbULO7k83CtnEiMfmUjB4zjkcCs3xdpm/43+DE+33y/aI7xsrNgxYiY4Tj5RXQeJdJv/HsOm6ZcaXc6bp0V2l1evdPGWcJnESBGbJJPLHAAHGap+JdM166+K3hnW7TQrmfTtLSdJplngUt5iFQVVpASBkHkCgD0G2g+zW0cPmyy7BjfK25m+p715b42sDcfGzwSv228jE8V0P3c23y9sR+56E9z3r1ZSWUEqVJGSp6j24rz/xxo+snxz4T8S6Vpj6lHprTx3EEUqI4WRdoYbyAep7+lAGT4utblvjL4ItbS9eGb7FdILl1EjgeWwLc8FsZ5ORnkg9Ku6Zdar4c+L0XhqXWr7U9N1DTDdqL5ld4pFYjhgBxhTxjv7Uusab4ivPin4X19fD8xstPt5o7lo7mE4aRGA2hnBOMjPA74zUuoaVrk3xp0zXotFuG0m2sGs5LjzoR8zFjuCl923kds9eKAKdn4gvvGNhqN9D/AG/DF9omhsG00IqIqHaHbJy7Egkg8YwAOpPU+Ab3xHe+FIG8VWTWurRO0cmQo80Do+F4GQcH3Brj9C0/xp8PNT1LS9O8Prrug3V09zZyR3kcLwF+qMH7cD2798V6Noyan9lafV2iW6mbf5ELbo4FxgIGIG71LYGST0GKAPP/AI8wGTwDFKJ50CXsIKI+FfJ/iHfGOKm+K11rHhP4dzXuja5exTRzIrPJtkd1cgY3EZXHbFanxX8O6l4m8CXFlpMQmvY5op44iwXftbkAnjOCfyrI+I1j4m8Z/Dt9PtPDNxFe3E0biF7qDMaqQSXJcDk5wBn3x0ABuzaH4rsv7W1Ky8RNe3tzabbaxuY1W3gnz95SOcAcAHOe5Nc1rOv3Xhy68Im11q4vby51KCw1UeY00EhcYfBxsRgwyAu0+oxXUeNrPXvEHw51G10eGax1a4iULDJKgfG4Fk3KxUZXcM579q4zXtK8Wav4X8Ki18Ii0/sXULadrIXcW9xGCDswdoX6nPPTigDe8WatrGm/FHwdYwapKNN1KSfzrQIgX92gx82NxB3dCe1QeJtQ16L4veH9Fstdnt7HULWeR4vKjYRlUbleBk8cbtwB5welL4q03xDqnj7wdq9v4euHttKMr3TLcwceYqjC5cE4xzwPbNS67pWuXPxd8O67baJcS6Zp9vLFPMJoQcyKwGFLgkDIzx9M0ARaDearonxcvPCtxrN5qenz6WL+Fr0q0kT79pAYAcdeMelS+EtV1if4o+L9Gv8AVZru1sI7f7MGRE2b13HhQATzjJ9KG0rXP+F2jxB/YtwdJGmfYPtAmh5bfu3bd+7b26Z9qTwxpeu2nxU8Ua3eaHcwafqiQrBK08DEeWgU7lWQkZxx1oAz/DTeIPEfi3xpo154o1COz0+5ijikgWNJgpDkAMF2qOOcLk4HI5BvfD3xNqsvhPxJ/ak76ldaFfXNskpADzrGuVzjv1H5U7wTpeuaV418Y6jf6HcwWuqTpNav58DFggcYIEhwTkY7epFReALDWfDNp4tudY0v+z1ur+41GCS5uYjHtYZAco7FcY5OMYoAxbnWdcvvgwPHFl4guodYVTcPsZTBgSlTF5RBUADocZJHJOa9Q8Oi9Og2cuoXU1zczRLK7SoiMpYA7cIAOOnSvJ9Dk1WysI3uvhdfT/aJBdTC3vx9nlkJ3bxb5CL2IBGRgZORmvW9C1iLXdJiv47e4ttxZHguU2SROrFWVh6gg0AYfibXbpPFGheFtPn+zT6n5ss9yAC0UMa5IQHjcx4BIOOTjpUkGi6/p3jG3uLPVmn8PSW7Ld2t5K0kiyjO1o2IzzxkZxweOmMn4heGddu9V0TxR4XEMusaQ7j7NMwVbiJxhlycDPXqR1POQK19A1HxVrU8U2raGmg2sWS8TXS3Es7YwB8owqjrnqSB0GcgGT4V1S48fza5fyX91badaX72NnBaS+WcIFJlZhySS3AzgAdD1qloPinxBb6j4w8K3LDUtX0a3Nxp07IA1yjJujVwMAtkoCRjOan8N6Dq3gLX9cit9Pm1LQdTuDeQNbOnmW0p+8jK7LkHjBBP3RnrxPZeHNZtrnxb4rWFY9e1S32WNpvU+SI48RqzZ27mYAnkgetAFHwV4htvFdppU2n+ILsazaup1exupSGk+UiQeWeFAYggoAOMH0rPstLM/wC0JrcZ1C/T/iUpKGSfBGXT5B6L7Vo674Tk8Sa/oGs2mgzaNrVrexT3l6XjGIl++hKMfMJ4A46dcDipLvSde0n4xTeIbLR21Gwv9MW0LpOkfkyBgctuOduF6gHrwCRigCK98W28PxD1bQfEupXekIyRf2PKsphidSvzNuHDNuJHz5XjGM5z32jw3lvotjDqFwLi8jgRJ5x/y0cAAt+J5rldZ0+XX4NW0vxJ4Xl1Gz84mwlhaIkqUH3SWDIwbcMnHGOa1PAei3/h3wPpWk6ncefeW0RWRg24DLEhQe4UEL+FAGd4y8R3dp4j8OeGNPn+zXOszP5tyAC0MMa7m2g8bm6AkHGDx0rL8b6pqPw8l0vXbXULq60iS6W21Czu5PNwrZxIjH5lIweM45HArQ8feFNQ1e+0PxBonltq2iXBlSCR9i3EZxvj3dicYBPHJpviXSb/AMew6bplxpdzpunRXaXV6908ZZwmcRIEZskk8scAAcZoAwPHum7/AIteBB9uvV+0y3f3ZseViNPucfLnvXqdpb/ZbVIPOlm2DHmTNudvqa4Dxhpeu3/xH8J6pY6Fc3FjpDzG4lWeBdwkVV+UNICcYzyBXoaMWRWKFCQCVbGR7HHFAHlXjawNx8bPBK/bbyMTxXQ/dzbfL2xH7noT3Pejxda3LfGXwRa2l68M32K6QXLqJHA8tgW54LYzycjPJB6VreONH1k+OfCfiXStMfUo9NaeO4gilRHCyLtDDeQD1Pf0qvrGm+Irz4p+F9fXw/MbLT7eaO5aO5hOGkRgNoZwTjIzwO+M0AJpl1qvhz4vReGpdavtT03UNMN2ovmV3ikViOGAHGFPGO/tTbTxDd+Lv7XmQa9FbQ3ktpZtpYRVUJxvYkgsxbJweAMDHUmxqGla5N8adM16LRbhtJtrBrOS486EfMxY7gpfdt5HbPXis7S9O8ZfD/X9XttL0Aa7oGo3bXlv5d2kMlu79VO/twB6cA5ySKAOq+Hl54mu/C6r4ss2t9UglaIuwUGZAAVfC8A8kfhXV1maKuqtbvc6x5UdzM24W0Lbkt17LuwNzdSWwOTgcAVp0AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVPVbyOw0q5upYvNjjQlk/vD0q5VTVLEanplxZGQxiZdpYDOPwoA890fxxFo9kbOPT3khWRmizNgqpOcHjnknmtD/AIWZH/0Cm/7/AP8A9jXZDStPUACxtsDj/VL/AIUv9maf/wA+Nt/35X/CgDz/AFfx8NT0q4sk09ojMu3eZs4GeeMV6VWF4h03S00C9aW3ihQR5MkUK7l54x07+9btABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSMCVIBIJHUdqAForzjwNrt2PEnjaHW9aea20++jhhlu3RFjXDccBVHboBnFeiRSxzxLLFIskbDKuhyCPY0APoqsuo2T3jWa3lu10vJgEqlx/wHOaddXtpYxiS7uobdCdoaaQICfTJoAnorH17xNpXh3w/PrV9dRiziUkFGBMjdlX1JPFW9K1O31bToLu3kjYSRq7Kjh9hIBwSO/NAF2imSzRQJvmlSNcgbnYAZPQc1El/ZyXj2aXcDXSDc0AkBdR6lc5FAFiuN1TwTqD+KLnxD4f8STaTeXkaR3cclstzFKEGFO0kbSB6H+Zz1UF/Z3M8sEF3BLNFxJHHIGZPqAcilur20sYvNvLmG3jzjfNIEH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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": null,
          "metadata": {
            "image/png": {
              "height": 600,
              "width": 500
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Image\n",
        "from pdf2image import convert_from_path\n",
        "\n",
        "\n",
        "# download the PDF file\n",
        "pdf_url = \"https://arxiv.org/pdf/1706.03762.pdf\" # @param\n",
        "pdf_filename = pdf_url.split(\"/\")[-1]\n",
        "!wget -q $pdf_url\n",
        "\n",
        "## visualize the pdf as an image\n",
        "# convert the PDF file to images\n",
        "images = convert_from_path(pdf_filename, 200)\n",
        "for image in images:\n",
        "  image.save('cover.png', \"PNG\")\n",
        "  break\n",
        "\n",
        "# show the pdf first page\n",
        "Image('cover.png', width=500, height=600)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgTSp9bB9zsx"
      },
      "source": [
        "Now you will create your reference structure. It will be a Python `Class` that will refer to the title, authors, abstract and keywords from the paper."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "he0EID4N8Nlo"
      },
      "outputs": [],
      "source": [
        "from pydantic import BaseModel\n",
        "\n",
        "\n",
        "class ResearchPaperExtraction(BaseModel):\n",
        "    title: str\n",
        "    authors: list[str]\n",
        "    abstract: str\n",
        "    keywords: list[str]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjdvGc1S-AuB"
      },
      "source": [
        "Now you will do your request to the Gemini API sending the pdf file and the reference structure."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5J96q3NN-Q5k"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"title\": \"Attention Is All You Need\",\n",
            "  \"authors\": [\n",
            "    \"Ashish Vaswani\",\n",
            "    \"Noam Shazeer\",\n",
            "    \"Niki Parmar\",\n",
            "    \"Jakob Uszkoreit\",\n",
            "    \"Llion Jones\",\n",
            "    \"Aidan N. Gomez\",\n",
            "    \"Łukasz Kaiser\",\n",
            "    \"Illia Polosukhin\"\n",
            "  ],\n",
            "  \"abstract\": \"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\",\n",
            "  \"keywords\": [\n",
            "    \"sequence transduction\",\n",
            "    \"attention mechanisms\",\n",
            "    \"machine translation\",\n",
            "    \"encoder-decoder architecture\",\n",
            "    \"self-attention\",\n",
            "    \"neural networks\",\n",
            "    \"transformer model\",\n",
            "    \"constituency parsing\"\n",
            "  ]\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "from pdfminer.high_level import extract_text\n",
        "\n",
        "\n",
        "# extract text from the PDF\n",
        "pdf_text = extract_text(pdf_filename)\n",
        "\n",
        "prompt = \"\"\"\n",
        "    As a specialist in knowledge organization and data refinement, your task is to transform\n",
        "    raw research paper content into a clearly defined structured format. I will provide you\n",
        "    with the original, free-form text. Your goal is to parse this text, extract the pertinent\n",
        "    information, and reconstruct it according to the structure outlined below.\n",
        "\"\"\"\n",
        "\n",
        "# send your request to the Gemini API\n",
        "completion = client.beta.chat.completions.parse(\n",
        "  model=MODEL_ID,\n",
        "  messages=[\n",
        "    {\"role\": \"system\", \"content\": prompt},\n",
        "    {\"role\": \"user\", \"content\": pdf_text}\n",
        "  ],\n",
        "  response_format=ResearchPaperExtraction,\n",
        ")\n",
        "\n",
        "print(completion.choices[0].message.parsed.model_dump_json(indent=2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUqDWEwnBp6Z"
      },
      "source": [
        "Given the Gemini API ability to handle structured outputs, you can work in more complex scenarios too - like using the structured output functionality to help you generating user interfaces.\n",
        "\n",
        "First you define the Python classes that represent the structure you want in the output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OSjclcbQBhb1"
      },
      "outputs": [],
      "source": [
        "from enum import Enum\n",
        "\n",
        "\n",
        "class UIType(str, Enum):\n",
        "    div = \"div\"\n",
        "    button = \"button\"\n",
        "    header = \"header\"\n",
        "    section = \"section\"\n",
        "    field = \"field\"\n",
        "    form = \"form\"\n",
        "\n",
        "class Attribute(BaseModel):\n",
        "    name: str\n",
        "    value: str\n",
        "\n",
        "class UI(BaseModel):\n",
        "    type: UIType\n",
        "    label: str\n",
        "    children: list[str]\n",
        "    attributes: list[Attribute]\n",
        "\n",
        "UI.model_rebuild() # This is required to enable recursive types\n",
        "\n",
        "class Response(BaseModel):\n",
        "    ui: UI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5t5SxwtyBhTx"
      },
      "source": [
        "Now you send your request using the `Response` class as the `response_format`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uswf40wyBhH-"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"ui\": {\n",
            "    \"attributes\": [],\n",
            "    \"children\": [\n",
            "      \"profileForm\"\n",
            "    ],\n",
            "    \"type\": \"div\",\n",
            "    \"label\": \"User Profile Container\"\n",
            "  }\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "completion = client.beta.chat.completions.parse(\n",
        "    model=MODEL_ID,\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": \"You are a UI generation assistant. Convert the user input into a UI.\"},\n",
        "        {\"role\": \"user\", \"content\": \"Make a User Profile Form including all required attributes\"}\n",
        "    ],\n",
        "    response_format=Response,\n",
        ")\n",
        "\n",
        "print(completion.choices[0].message.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OinLScbpbBo3"
      },
      "source": [
        "## Developing with the Gemini API Function Calling\n",
        "\n",
        "The Gemini API's function calling feature allows you to extend the model's capabilities by providing descriptions of external functions or APIs.\n",
        "\n",
        "For further understanding of how function calling works with Gemini models, check the [Gemini API documentation](https://ai.google.dev/gemini-api/docs/function-calling)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uuHXOyNocIEZ"
      },
      "outputs": [],
      "source": [
        "tools = [\n",
        "  {\n",
        "    \"type\": \"function\",\n",
        "    \"function\": {\n",
        "      \"name\": \"get_weather\",\n",
        "      \"description\": \"Gets the weather at the user's location\",\n",
        "      \"parameters\": {\n",
        "        \"type\": \"object\",\n",
        "        \"properties\": {\n",
        "          \"location\": {\"type\": \"string\"},\n",
        "        },\n",
        "      },\n",
        "    },\n",
        "  }\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euzAa4RWcIiX"
      },
      "source": [
        "Now you add the `tools` structure on your request."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mu2NRaGxcJMR"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ChatCompletionMessageToolCall(id='', function=Function(arguments='{\"location\":\"Boston\"}', name='get_weather'), type='function')\n"
          ]
        }
      ],
      "source": [
        "prompt = \"\"\"\n",
        "    What's the weather like in Boston?\n",
        "\"\"\"\n",
        "\n",
        "completion = client.chat.completions.create(\n",
        "  model=MODEL_ID,\n",
        "  messages=[{\"role\": \"user\", \"content\": prompt}],\n",
        "  tools=tools,\n",
        ")\n",
        "\n",
        "print(completion.choices[0].message.tool_calls[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8174569d2752"
      },
      "source": [
        "## Thinking\n",
        "\n",
        "Gemini 2.5 and 3 models are trained to think through complex problems, leading to significantly improved reasoning. The Gemini API comes with a [\"thinking budget\" parameter](https://ai.google.dev/gemini-api/docs/thinking) which gives fine grain control over how much the model will think.\n",
        "\n",
        "Unlike the Gemini API, the OpenAI API offers three levels of thinking control: \"low\", \"medium\", and \"high\". Here's the correspondance:\n",
        "\n",
        "| Open AI <br> reasoning effort | Gemini 2.5 <br> thinking budget | Gemini 3 Pro <br> thinking level |\n",
        "| --------| ----------| ----------- |\n",
        "| none | 0* | N/A |\n",
        "| minimal | 1024 | low |\n",
        "| low | 1024 | low |\n",
        "| medium | 8192 | high |\n",
        "| high | 24576 | high |\n",
        "\n",
        "If you want to disable thinking, you can set the reasoning effort to \"none\" but keep in mind that thinking can't be turned off on pro models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "966b84117583"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "Okay, let's break this down using the order of operations (often remembered by acronyms like PEMDAS or BODMAS). The order is:\n",
              "\n",
              "1.  **P**arentheses (or **B**rackets)\n",
              "2.  **E**xponents (or **O**rders)\n",
              "3.  **M**ultiplication and **D**ivision (from left to right)\n",
              "4.  **A**ddition and **S**ubtraction (from left to right)\n",
              "\n",
              "Here's the expression: `45 - 78 + 5 x 13`\n",
              "\n",
              "1.  **Multiplication:** We have `5 x 13`.\n",
              "    `5 x 13 = 65`\n",
              "    Now the expression becomes: `45 - 78 + 65`\n",
              "\n",
              "2.  **Addition and Subtraction (from left to right):**\n",
              "    *   First, do the subtraction: `45 - 78`.\n",
              "        `45 - 78 = -33`\n",
              "    *   Now, the expression is: `-33 + 65`\n",
              "    *   Finally, do the addition: `-33 + 65`.\n",
              "        `-33 + 65 = 32`\n",
              "\n",
              "The answer is **32**.\n",
              "\n",
              "**Double Check and Explanation:**\n",
              "\n",
              "*   **Step 1: Identify Operations:** We have subtraction, addition, and multiplication.\n",
              "*   **Step 2: Apply Order of Operations:** PEMDAS/BODMAS tells us Multiplication comes before Addition/Subtraction.\n",
              "*   **Step 3: Calculate Multiplication:** `5 x 13 = 65`. The expression is now `45 - 78 + 65`.\n",
              "*   **Step 4: Apply Left-to-Right for Addition/Subtraction:**\n",
              "    *   Do `45 - 78` first. This is subtracting a larger number from a smaller one, resulting in a negative number. `78 - 45 = 33`, so `45 - 78 = -33`. The expression is now `-33 + 65`.\n",
              "    *   Do `-33 + 65`. This is the same as `65 - 33`. `65 - 33 = 32`.\n",
              "\n",
              "The answer is 32. This is correct because we strictly followed the established order of operations, performing multiplication before addition/subtraction, and then handling the addition and subtraction from left to right."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "prompt = \"\"\"\n",
        "    What is 45-78+5x13?\n",
        "    Double check and explain why your answer is correct.\n",
        "\"\"\"\n",
        "\n",
        "response = client.chat.completions.create(\n",
        "  model=MODEL_ID,\n",
        "  reasoning_effort=\"low\",\n",
        "  messages=[\n",
        "      {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
        "      {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": prompt\n",
        "      }\n",
        "  ]\n",
        ")\n",
        "\n",
        "Markdown(response.choices[0].message.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xYoYJQqAq0Z"
      },
      "source": [
        "## Batch predictions\n",
        "\n",
        "In addition to real-time generations, the Gemini API provides a compatible layer for performing batch generations.\n",
        "\n",
        "Prepare a JSONL file in OpenAI batch input format. Note that the URLs and overall structure uses the OpenAI syntax, but the models are Gemini API model identifiers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VPF3RprWy6iX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing batch_requests.jsonl\n"
          ]
        }
      ],
      "source": [
        "%%writefile batch_requests.jsonl\n",
        "{\"custom_id\": \"request-1\", \"method\": \"POST\", \"url\": \"/v1/chat/completions\", \"body\": {\"model\": \"gemini-2.5-flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Tell me a one-sentence joke.\"}]}}\n",
        "{\"custom_id\": \"request-2\", \"method\": \"POST\", \"url\": \"/v1/chat/completions\", \"body\": {\"model\": \"gemini-2.5-flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Why is the sky blue?\"}]}}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIOPFlJtzMiZ"
      },
      "source": [
        "Now upload the JSONL file with the GenAI SDK. Until there is support for the OpenAI file upload API, you must use the Google GenAI SDK to upload the file to make the data available to the Gemini API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KkaOgv0TBF7B"
      },
      "outputs": [],
      "source": [
        "%pip install -qU google-genai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "diojG4H7y775"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "uploaded_file.name='files/7ker0qnexf7w'\n"
          ]
        }
      ],
      "source": [
        "# Upload JSONL file in OpenAI batch input format...\n",
        "from google import genai\n",
        "from google.genai import types\n",
        "\n",
        "genai_client = genai.Client(api_key=GOOGLE_API_KEY)\n",
        "\n",
        "uploaded_file = genai_client.files.upload(\n",
        "    file=\"batch_requests.jsonl\",\n",
        "    config=types.UploadFileConfig(display_name=\"my-batch-requests\", mime_type=\"jsonl\"),\n",
        ")\n",
        "print(f'{uploaded_file.name=}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-NNnQXOzSsq"
      },
      "source": [
        "Create a batch generation job referencing the file just uploaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3QImQuXxy-Qx"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "batch.id='batches/l6htkjfcw75hchaok5y422p8sd12fpj7lh00'\n"
          ]
        }
      ],
      "source": [
        "batch = client.batches.create(\n",
        "    input_file_id=uploaded_file.name,\n",
        "    endpoint=\"/v1/chat/completions\",\n",
        "    completion_window=\"24h\"\n",
        ")\n",
        "print(f'{batch.id=}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmmqnSaMzWif"
      },
      "source": [
        "Batches can take up to 24 hours to process. Poll here with the following code, or come back later and replace `batch.id` with the ID printed during creation above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vy5A2zmyzHyF"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "batch.status='completed'\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "while (batch := client.batches.retrieve(batch.id)).status == 'in_progress':\n",
        "    print(f\"Job not finished. Current state: {batch.status}. Waiting 30 seconds...\")\n",
        "    time.sleep(30)\n",
        "\n",
        "print(f'{batch.status=}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZwWj6gkzZC1"
      },
      "source": [
        "Now that the batch is processed, download the results and print them out."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "689YpnarzJpj"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 {\"response\":{\"status_code\":200,\"body\":{\"id\":\"okvAaJuhOdSFjrEP3rDF2QI\",\"usage\":{\"completionTokens\":19,\"promptTokens\":9,\"totalTokens\":488},\"object\":\"chat.completion\",\"choices\":[{\"message\":{\"role\":\"assistant\",\"content\":\"I'm reading a book about anti-gravity; it's impossible to put down!\"},\"finishReason\":\"stop\"}],\"created\":\"1757432746\",\"model\":\"gemini-2.5-flash\"}},\"custom_id\":\"request-1\"}\n",
            "2 {\"custom_id\":\"request-2\",\"response\":{\"status_code\":200,\"body\":{\"id\":\"qkvAaOCnONOG-8YPpN-yqQk\",\"usage\":{\"completionTokens\":589,\"totalTokens\":1893,\"promptTokens\":7},\"choices\":[{\"message\":{\"content\":\"The sky is blue due to a phenomenon called **Rayleigh scattering**. Here's a breakdown:\\n\\n1.  **Sunlight is White Light:** Sunlight, which appears white to us, is actually made up of all the colors of the rainbow (red, orange, yellow, green, blue, indigo, violet). You can see this when light passes through a prism or when a rainbow forms. These colors all have different **wavelengths** – blue and violet light have shorter, smaller wavelengths, while red and orange light have longer, larger wavelengths.\\n\\n2.  **Earth's Atmosphere:** Our planet is surrounded by an atmosphere composed mostly of tiny gas molecules, primarily nitrogen (about 78%) and oxygen (about 21%). There are also smaller amounts of other gases, dust, and water vapor.\\n\\n3.  **Scattering:** When sunlight enters the Earth's atmosphere, it interacts with these tiny gas molecules. The light \\\"bumps\\\" into these molecules and gets scattered, or bounced, in all directions.\\n\\n4.  **Wavelength Matters:** This is the crucial part: **The amount of scattering depends heavily on the wavelength of the light and the size of the particles it hits.**\\n    *   The tiny nitrogen and oxygen molecules in our atmosphere are much smaller than the wavelengths of visible light.\\n    *   For such small particles, shorter-wavelength colors (like blue and violet) are scattered much more effectively and intensely than longer-wavelength colors (like red, orange, and yellow). Blue light is scattered about 10 times more than red light.\\n\\n5.  **Why We See Blue:** Because blue and violet light are scattered so much more than other colors, they spread out across the entire sky. When you look up at any point in the sky, you are seeing this scattered blue light coming from all directions.\\n\\n6.  **Why Not Violet?** You might wonder why the sky isn't violet, since violet light has an even shorter wavelength than blue and is scattered slightly more. There are two main reasons:\\n    *   The sun emits slightly less violet light than blue light.\\n    *   Our eyes are more sensitive to blue light than to violet light. So, even though some violet light is scattered, our eyes perceive the mixture of blue and violet as predominantly blue.\\n\\n**In summary:** The tiny gas molecules in Earth's atmosphere efficiently scatter the shorter-wavelength blue light from the sun in all directions, making the sky appear blue to our eyes.\\n\\nThis same principle also explains why sunsets and sunrises are often red and orange. When the sun is low on the horizon, its light has to travel through a much thicker layer of atmosphere. By the time it reaches your eyes, most of the blue and violet light has been scattered away, leaving primarily the longer-wavelength reds, oranges, and yellows.\",\"role\":\"assistant\"},\"finishReason\":\"stop\"}],\"created\":\"1757432746\",\"object\":\"chat.completion\",\"model\":\"gemini-2.5-flash\"}}}\n"
          ]
        }
      ],
      "source": [
        "if batch.status == 'completed':\n",
        "  # Download the output file.\n",
        "  file_content_bytes = genai_client.files.download(file=batch.output_file_id)\n",
        "  file_content = file_content_bytes.decode('utf-8')\n",
        "\n",
        "  # Print each output record.\n",
        "  for i, line in enumerate(file_content.splitlines(), start=1):\n",
        "      print(i, line)\n",
        "\n",
        "else:\n",
        "  print(f'An error occurred. Batch status is \"{batch.status}\".')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xp5kgzUmIBdj"
      },
      "source": [
        "## Generating and working with embeddings\n",
        "\n",
        "Text embeddings offer a compressed, vector-based representation of text, typically in a lower-dimensional space. The core principle is that semantically similar texts will have embeddings that are spatially proximate within the embedding vector space. This representation enables solutions to several prevalent NLP challenges, including:\n",
        "\n",
        "- **Semantic Search:** Identifying and ranking texts based on semantic relatedness.\n",
        "- **Recommendation:** Suggesting items whose textual descriptions exhibit semantic similarity to a given input text.\n",
        "- **Classification:** Assigning text to categories based on the semantic similarity between the text and the category's representative text.\n",
        "- **Clustering:** Grouping texts into clusters based on the semantic similarity reflected in their respective embedding vectors.\n",
        "- **Outlier Detection:** Identifying texts that are semantically dissimilar from the majority, as indicated by their distance in the embedding vector space.\n",
        "\n",
        "For more details about working with the Gemini API and embeddings, check the [API documentation](https://ai.google.dev/gemini-api/docs/embeddings).\n",
        "\n",
        "In this example you will use the `gemini-embedding-001` model from the Gemini API to generate your embeddings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NiNA7nrCZABg"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3072\n",
            "[-0.0108722522854805, 0.019908268004655838, -0.003474034368991852, -0.045275624841451645] ...\n"
          ]
        }
      ],
      "source": [
        "EMBEDDINGS_MODEL=\"gemini-embedding-001\"\n",
        "prompt = \"\"\"\n",
        "    The quick brown fox jumps over the lazy dog.\n",
        "\"\"\"\n",
        "\n",
        "response = client.embeddings.create(\n",
        "  model=EMBEDDINGS_MODEL,\n",
        "  input=prompt,\n",
        ")\n",
        "\n",
        "print(len(response.data[0].embedding))\n",
        "print(response.data[0].embedding[:4], '...')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xWGw7677ICOt"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3072\n",
            "[-0.0108722522854805, 0.019908268004655838, -0.003474034368991852, -0.045275624841451645] ...\n"
          ]
        }
      ],
      "source": [
        "EMBEDDINGS_MODEL=\"gemini-embedding-001\"\n",
        "prompt = \"\"\"\n",
        "    The quick brown fox jumps over the lazy dog.\n",
        "\"\"\"\n",
        "\n",
        "response = client.embeddings.create(\n",
        "  model=EMBEDDINGS_MODEL,\n",
        "  input=prompt,\n",
        ")\n",
        "\n",
        "print(len(response.data[0].embedding))\n",
        "print(response.data[0].embedding[:4], '...')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9P0Ls4lJl-U"
      },
      "source": [
        "A simple application of text embeddings is to calculate the similarity between sentences (ie. product reviews, documents contents, etc). First you will create a group of sentences."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YTxKsukQJmov"
      },
      "outputs": [
        {
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "\n",
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              "        background-color: #E8F0FE;\n",
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              "        display: none;\n",
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              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "    </button>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                text\n",
              "0              i really enjoyed the movie last night\n",
              "1         so many amazing cinematic scenes yesterday\n",
              "2  had a great time writing my Python scripts a f...\n",
              "3  huge sense of relief when my .py script finall...\n",
              "4          O Romeo, Romeo, wherefore art thou Romeo?"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "text = [\n",
        "    \"i really enjoyed the movie last night\",\n",
        "    \"so many amazing cinematic scenes yesterday\",\n",
        "    \"had a great time writing my Python scripts a few days ago\",\n",
        "    \"huge sense of relief when my .py script finally ran without error\",\n",
        "    \"O Romeo, Romeo, wherefore art thou Romeo?\",\n",
        "]\n",
        "\n",
        "df = pd.DataFrame(text, columns=[\"text\"])\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPX1yzmtKBSH"
      },
      "source": [
        "Now you can create a function to generate embeddings, apply that function to your dataframe `text` column and save it into a new column called `embeddings`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VSIAkFPOKQwr"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"so many amazing cinematic scenes yesterday\",\n          \"O Romeo, Romeo, wherefore art thou Romeo?\",\n          \"had a great time writing my Python scripts a few days ago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embeddings\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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            "text/plain": [
              "                                                text  \\\n",
              "0              i really enjoyed the movie last night   \n",
              "1         so many amazing cinematic scenes yesterday   \n",
              "2  had a great time writing my Python scripts a f...   \n",
              "3  huge sense of relief when my .py script finall...   \n",
              "4          O Romeo, Romeo, wherefore art thou Romeo?   \n",
              "\n",
              "                                          embeddings  \n",
              "0  [-0.015758849680423737, -0.00223345635458827, ...  \n",
              "1  [-0.01200825534760952, 0.007556390017271042, 0...  \n",
              "2  [0.0065507469698786736, 0.0018844676669687033,...  \n",
              "3  [-0.008062172681093216, -0.006349125877022743,...  \n",
              "4  [-0.02323131076991558, 0.0021875633392482996, ...  "
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def generate_embeddings(text):\n",
        "  response = client.embeddings.create(\n",
        "    model=EMBEDDINGS_MODEL,\n",
        "    input=text,\n",
        "  )\n",
        "  return response.data[0].embedding\n",
        "\n",
        "df[\"embeddings\"] = df.apply(\n",
        "    lambda x: generate_embeddings([x.text]), axis=1\n",
        ")\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kq_ckL1IK1ww"
      },
      "source": [
        "Now that you have the embeddings representations for all sentences, you can calculate their similarities."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g8Opxn9DK0_T"
      },
      "outputs": [
        {
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                                    i really enjoyed the movie last night  \\\n",
              "i really enjoyed the movie last night                                            1.000000   \n",
              "so many amazing cinematic scenes yesterday                                       0.653648   \n",
              "had a great time writing my Python scripts a fe...                               0.645156   \n",
              "huge sense of relief when my .py script finally...                               0.602606   \n",
              "O Romeo, Romeo, wherefore art thou Romeo?                                        0.508644   \n",
              "\n",
              "                                                    so many amazing cinematic scenes yesterday  \\\n",
              "i really enjoyed the movie last night                                                 0.653648   \n",
              "so many amazing cinematic scenes yesterday                                            1.000000   \n",
              "had a great time writing my Python scripts a fe...                                    0.649567   \n",
              "huge sense of relief when my .py script finally...                                    0.611159   \n",
              "O Romeo, Romeo, wherefore art thou Romeo?                                             0.528216   \n",
              "\n",
              "                                                    had a great time writing my Python scripts a few days ago  \\\n",
              "i really enjoyed the movie last night                                                        0.645156           \n",
              "so many amazing cinematic scenes yesterday                                                   0.649567           \n",
              "had a great time writing my Python scripts a fe...                                           1.000000           \n",
              "huge sense of relief when my .py script finally...                                           0.810877           \n",
              "O Romeo, Romeo, wherefore art thou Romeo?                                                    0.476175           \n",
              "\n",
              "                                                    huge sense of relief when my .py script finally ran without error  \\\n",
              "i really enjoyed the movie last night                                                        0.602606                   \n",
              "so many amazing cinematic scenes yesterday                                                   0.611159                   \n",
              "had a great time writing my Python scripts a fe...                                           0.810877                   \n",
              "huge sense of relief when my .py script finally...                                           1.000000                   \n",
              "O Romeo, Romeo, wherefore art thou Romeo?                                                    0.462184                   \n",
              "\n",
              "                                                    O Romeo, Romeo, wherefore art thou Romeo?  \n",
              "i really enjoyed the movie last night                                                0.508644  \n",
              "so many amazing cinematic scenes yesterday                                           0.528216  \n",
              "had a great time writing my Python scripts a fe...                                   0.476175  \n",
              "huge sense of relief when my .py script finally...                                   0.462184  \n",
              "O Romeo, Romeo, wherefore art thou Romeo?                                            1.000000  "
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "cos_sim_array = cosine_similarity(list(df.embeddings.values))\n",
        "\n",
        "# display as DataFrame\n",
        "analysis = pd.DataFrame(cos_sim_array, index=text, columns=text)\n",
        "analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4JHJWhlLLEv"
      },
      "source": [
        "You can also plot it for a better visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DqMZYAEmLK1-"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Text(0.5, 1, 'i really enjoyed the movie last night'),\n",
              " Text(1.5, 1, 'so many amazing cinematic scenes yesterday'),\n",
              " Text(2.5, 1, 'had a great time writing my Python scripts a few days ago'),\n",
              " Text(3.5, 1, 'huge sense of relief when my .py script finally ran without error'),\n",
              " Text(4.5, 1, 'O Romeo, Romeo, wherefore art thou Romeo?')]"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "ax = sns.heatmap(analysis, annot=True, cmap=\"Blues\")\n",
        "ax.xaxis.tick_top()\n",
        "ax.set_xticklabels(text, rotation=90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tW5LI7gXnHN"
      },
      "source": [
        "### Batch embedding generation\n",
        "\n",
        "You can use the OpenAI SDK to generate embeddings offline and in large batches at a reduced rate. The API follows the same steps you would take for generating content.\n",
        "\n",
        "Start by creating a JSONL file that contains each of the embedding requests to process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "siZB5oCVXxP4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing embedding_requests.jsonl\n"
          ]
        }
      ],
      "source": [
        "%%writefile embedding_requests.jsonl\n",
        "{\"custom_id\": \"request-1\", \"method\": \"POST\", \"url\": \"/v1/embeddings\", \"body\": {\"model\": \"gemini-embedding-001\", \"input\": \"I really enjoyed the movie last night\"}}\n",
        "{\"custom_id\": \"request-2\", \"method\": \"POST\", \"url\": \"/v1/embeddings\", \"body\": {\"model\": \"gemini-embedding-001\", \"input\": \"So many amazing cinematic scenes yesterday\"}}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20ECVGohZqIS"
      },
      "source": [
        "Upload the JSONL file containing the embedding requests. Until there is support for the OpenAI file upload API, you must use the Google GenAI SDK to upload the file and make the data available to the Gemini API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IlXMPUXrZTJX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "uploaded_file.name='files/ade20ut10uif'\n"
          ]
        }
      ],
      "source": [
        "from google import genai\n",
        "from google.genai import types\n",
        "\n",
        "genai_client = genai.Client(api_key=GOOGLE_API_KEY)\n",
        "\n",
        "uploaded_file = genai_client.files.upload(\n",
        "    file=\"embedding_requests.jsonl\",\n",
        "    config=types.UploadFileConfig(display_name=\"My embedding requests\", mime_type=\"jsonl\"),\n",
        ")\n",
        "print(f'{uploaded_file.name=}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrkfgTTJZr05"
      },
      "source": [
        "Now, with the file uploaded, create a new batch job to process the requests in the JSONL file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pJ9mvPdmZrmd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "batch.id='batches/uwmtevupo07d4p5nsnsxh72nrsrym9txgime'\n"
          ]
        }
      ],
      "source": [
        "batch = client.batches.create(\n",
        "    input_file_id=uploaded_file.name,\n",
        "    endpoint=\"/v1/embeddings\",\n",
        "    completion_window=\"24h\"\n",
        ")\n",
        "print(f'{batch.id=}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXPdCp-OarZu"
      },
      "source": [
        "Batches can take up to 24 hours to process. Poll here with the following code, or come back later and replace `batch.id` with the ID printed during creation above."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W9T9rLH3Z20a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Job not finished. Current state: in_progress. Waiting 30 seconds...\n",
            "Job not finished. Current state: in_progress. Waiting 30 seconds...\n",
            "Job not finished. Current state: in_progress. Waiting 30 seconds...\n",
            "batch.status='completed'\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "while (batch := client.batches.retrieve(batch.id)).status == 'in_progress':\n",
        "    print(f\"Job not finished. Current state: {batch.status}. Waiting 30 seconds...\")\n",
        "    time.sleep(30)\n",
        "\n",
        "print(f'{batch.status=}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hCum79yax36"
      },
      "source": [
        "Now that the batch is processed, download the results and print them out."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e9up6SOEZ8KX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1 {\"response\":{\"status_code\":200,\"body\":{\"data\":[{\"object\":\"embedding\",\"embedding\":[-0.014900187961757...\n",
            "2 {\"custom_id\":\"request-2\",\"response\":{\"status_code\":200,\"body\":{\"model\":\"models/gemini-embedding-001\"...\n"
          ]
        }
      ],
      "source": [
        "if batch.status == 'completed':\n",
        "  # Download the output file.\n",
        "  file_content_bytes = genai_client.files.download(file=batch.output_file_id)\n",
        "  file_content = file_content_bytes.decode('utf-8')\n",
        "\n",
        "  # Print each output record.\n",
        "  for i, line in enumerate(file_content.splitlines(), start=1):\n",
        "      print(i, line[:100] + '...')\n",
        "\n",
        "else:\n",
        "  print(f'An error occurred. Batch status is \"{batch.status}\".')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SOkIVJIyF1W"
      },
      "source": [
        "## Next Steps\n",
        "\n",
        "### Do more with Gemini\n",
        "\n",
        "If you want to use more of the Gemini capabilities and especially its unique capabilities not available through the OpenAI compatibility, you should check out the [Google GenAI SDK](https://github.com/googleapis/python-genai).\n",
        "\n",
        "The Cookbook is full of examples on how to use it but it is recommended to start with the [Getting started ![image](https://storage.googleapis.com/generativeai-downloads/images/colab_icon16.png)](./Get_started.ipynb) notebook to get a feel of all the models and SDK capabilities.\n",
        "\n",
        "### Related examples\n",
        "\n",
        "Check the rest of the [Cookbook](https://github.com/google-gemini/cookbook). You'll learn how to use the [Live API ![image](https://storage.googleapis.com/generativeai-downloads/images/colab_icon16.png)](./Get_started_LiveAPI.ipynb), juggle with [multiple tools ![image](https://storage.googleapis.com/generativeai-downloads/images/colab_icon16.png)](../examples/LiveAPI_plotting_and_mapping.ipynb) or use Gemini's [spatial understanding ![image](https://storage.googleapis.com/generativeai-downloads/images/colab_icon16.png)](./Spatial_understanding.ipynb) abilities.\n",
        "\n",
        "Also check the [Thinking guide ![image](https://storage.googleapis.com/generativeai-downloads/images/colab_icon16.png)](./Get_started_thinking.ipynb) that explicitly showcases its thoughts and can manage more complex reasonings."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Get_started_OpenAI_Compatibility.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
